Evidence-based action plan for integrating artificial intelligence in an academic medical centre-a multidisciplinary approach.

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Learn more: PMC Disclaimer | PMC Copyright Notice PLoS One . 2026 Apr 17;21(4):e0346582. doi: 10.1371/journal.pone.0346582 Search in PMC Search in PubMed View in NLM Catalog Add to search Evidence-based action plan for integrating artificial intelligence in an academic medical centre-a multidisciplinary approach Lena Jafri Lena Jafri 1 Section of Chemical Pathology, Department of Pathology and Laboratory Medicine Aga Khan University (AKU), Karachi Pakistan Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft Find articles by Lena Jafri 1, * , Hafsa Majid Hafsa Majid 1 Section of Chemical Pathology, Department of Pathology and Laboratory Medicine Aga Khan University (AKU), Karachi Pakistan Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – review & editing Find articles by Hafsa Majid 1 , Aysha Habib Khan Aysha Habib Khan 1 Section of Chemical Pathology, Department of Pathology and Laboratory Medicine Aga Khan University (AKU), Karachi Pakistan Funding acquisition, Methodology, Validation, Writing – review & editing Find articles by Aysha Habib Khan 1 , Aqueel Kapadia Aqueel Kapadia 1 Section of Chemical Pathology, Department of Pathology and Laboratory Medicine Aga Khan University (AKU), Karachi Pakistan Data curation, Formal analysis, Investigation, Validation, Writing – original draft Find articles by Aqueel Kapadia 1 , Shanzay Rehman Shanzay Rehman 1 Section of Chemical Pathology, Department of Pathology and Laboratory Medicine Aga Khan University (AKU), Karachi Pakistan Investigation, Project administration, Resources, Validation, Writing – review & editing Find articles by Shanzay Rehman 1 , Kiran Hilal Kiran Hilal 2 Department of Radiology, AKU, Pakistan Investigation, Methodology, Validation, Writing – review & editing Find articles by Kiran Hilal 2 , Imran Siddiqui Imran Siddiqui 1 Section of Chemical Pathology, Department of Pathology and Laboratory Medicine Aga Khan University (AKU), Karachi Pakistan Investigation, Resources, Supervision, Writing – review & editing Find articles by Imran Siddiqui 1 , Arsala Farooqui Arsala Farooqui 3 Institute for Global Health and Development, AKU Conceptualization, Data curation, Investigation, Resources, Writing – review & editing Find articles by Arsala Farooqui 3 , Kulsoom Ghias Kulsoom Ghias 4 Department of Biological & Biomedical Sciences, AKU, Pakistan Funding acquisition, Methodology, Validation, Writing – review & editing Find articles by Kulsoom Ghias 4 , Naveed Rashid Naveed Rashid 5 Department of Medicine, AKU, Pakistan Investigation, Methodology, Validation, Writing – review & editing Find articles by Naveed Rashid 5 , Yousra Sarfaraz Yousra Sarfaraz 1 Section of Chemical Pathology, Department of Pathology and Laboratory Medicine Aga Khan University (AKU), Karachi Pakistan Formal analysis, Methodology, Validation, Writing – review & editing Find articles by Yousra Sarfaraz 1 , Muhammad Umer Naeem Effendi Muhammad Umer Naeem Effendi 1 Section of Chemical Pathology, Department of Pathology and Laboratory Medicine Aga Khan University (AKU), Karachi Pakistan Formal analysis, Methodology, Validation, Writing – review & editing Find articles by Muhammad Umer Naeem Effendi 1 , Syed Murtaza Raza Kazmi Syed Murtaza Raza Kazmi 6 Department of Surgery, AKU, Pakistan Methodology, Validation, Writing – review & editing Find articles by Syed Murtaza Raza Kazmi 6 , Muneeb Khalid Muneeb Khalid 7 Aga Khan Medical College, Pakistan Investigation, Methodology, Validation, Writing – review & editing Find articles by Muneeb Khalid 7 , Khairulnissa Ajani Khairulnissa Ajani 8 School of Nursing & Midwifery, AKU, Pakistan Investigation, Methodology, Validation, Writing – review & editing Find articles by Khairulnissa Ajani 8 , Qamar Riaz Qamar Riaz 9 Department of Post Graduate Medical Education, AKU, Pakistan Investigation, Methodology, Validation, Writing – review & editing Find articles by Qamar Riaz 9 , Mehmood Riaz Mehmood Riaz 5 Department of Medicine, AKU, Pakistan Investigation, Methodology, Validation, Writing – review & editing Find articles by Mehmood Riaz 5 , Rozina Roshan Rozina Roshan 10 Quality and Patient Safety Department and Department of Infection Prevention & Hospital Epidemiology, AKU, Pakistan Investigation, Methodology, Validation, Writing – review & editing Find articles by Rozina Roshan 10 , Sibtain Ahmed Sibtain Ahmed 1 Section of Chemical Pathology, Department of Pathology and Laboratory Medicine Aga Khan University (AKU), Karachi Pakistan Data curation, Formal analysis, Methodology, Validation, Writing – review & editing Find articles by Sibtain Ahmed 1 Editor: Syed Hani Abidi 11 Author information Article notes Copyright and License information 1 Section of Chemical Pathology, Department of Pathology and Laboratory Medicine Aga Khan University (AKU), Karachi Pakistan 2 Department of Radiology, AKU, Pakistan 3 Institute for Global Health and Development, AKU 4 Department of Biological & Biomedical Sciences, AKU, Pakistan 5 Department of Medicine, AKU, Pakistan 6 Department of Surgery, AKU, Pakistan 7 Aga Khan Medical College, Pakistan 8 School of Nursing & Midwifery, AKU, Pakistan 9 Department of Post Graduate Medical Education, AKU, Pakistan 10 Quality and Patient Safety Department and Department of Infection Prevention & Hospital Epidemiology, AKU, Pakistan 11 Nazarbayev University School of Medicine, KAZAKHSTAN Competing Interests: The authors have declared that no competing interests exist. ✉ * E-mail: [email protected] Roles Lena Jafri : Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft Hafsa Majid : Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – review & editing Aysha Habib Khan : Funding acquisition, Methodology, Validation, Writing – review & editing Aqueel Kapadia : Data curation, Formal analysis, Investigation, Validation, Writing – original draft Shanzay Rehman : Investigation, Project administration, Resources, Validation, Writing – review & editing Kiran Hilal : Investigation, Methodology, Validation, Writing – review & editing Imran Siddiqui : Investigation, Resources, Supervision, Writing – review & editing Arsala Farooqui : Conceptualization, Data curation, Investigation, Resources, Writing – review & editing Kulsoom Ghias : Funding acquisition, Methodology, Validation, Writing – review & editing Naveed Rashid : Investigation, Methodology, Validation, Writing – review & editing Yousra Sarfaraz : Formal analysis, Methodology, Validation, Writing – review & editing Muhammad Umer Naeem Effendi : Formal analysis, Methodology, Validation, Writing – review & editing Syed Murtaza Raza Kazmi : Methodology, Validation, Writing – review & editing Muneeb Khalid : Investigation, Methodology, Validation, Writing – review & editing Khairulnissa Ajani : Investigation, Methodology, Validation, Writing – review & editing Qamar Riaz : Investigation, Methodology, Validation, Writing – review & editing Mehmood Riaz : Investigation, Methodology, Validation, Writing – review & editing Rozina Roshan : Investigation, Methodology, Validation, Writing – review & editing Sibtain Ahmed : Data curation, Formal analysis, Methodology, Validation, Writing – review & editing Syed Hani Abidi : Editor Received 2025 Sep 1; Accepted 2026 Mar 21; Collection date 2026. © 2026 Jafri et al This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. PMC Copyright notice PMCID: PMC13089692  PMID: 41996369 Abstract Introduction As the effectiveness of artificial intelligence (AI) in enhancing various facets of healthcare delivery becomes more apparent, it is anticipated that AI will soon find its way into standard clinical practices, even in low and middle-income countries. The objective of this study was to create an action plan for integrating AI into medical education, research, and clinical practices utilizing the SWITCH model of change integrating both rational and emotional aspects. Methods This exploratory qualitative study employed reflexive thematic analysis of semi-structured interviews and a co-design workshop, followed by the collaborative development of an action plan. The study was conducted from May 2023 to May 2024, at the Aga Khan University, Karachi, Pakistan. The development of an action plan was informed by interviews, co-design workshop, and discussions with diverse group of academic leaders, healthcare professionals and medical students. All interviews, workshop sessions, and planning meetings were audio recorded, transcribed verbatim, and anonymized. Data management was conducted manually using Microsoft Word and Excel. Findings after thematic analysis of the qualitative interviews and findings from the co-design workshop were gradually, and inductively transformed into the content for the action plan for integrating AI into healthcare. Results The content analysis of interviews identified following four themes: AI opportunities, apprehensions toward AI-induced changes, pushing change through leadership styles, and importance of AI related capacity building. During the workshop, participants discussed aligning current AI knowledge with future requirements by identifying clear instructions, emotional motivators, and environmental changes required on the path of AI integration. The proposed action plan conceptualized AI integration as multidimensional change process comprising three domains: strategic actions, change pathway enablement and environmental readiness and human motivation operationalized through twenty actionable components. Conclusion The study findings provide a context specific conceptual action plan for healthcare professionals to integrate AI into medical education, clinical service and research, Future work should focus on pilot implementation and empirical validations of the action plan across diverse healthcare settings to assess feasibility, effectiveness and scalability. Introduction The healthcare system is undergoing a surge of disruptive innovations, including robotic surgery, 3D printing, wearable health devices, personalized medicine, telemedicine, and Artificial Intelligence (AI) integration [ 1 , 2 ]. AI-driven technologies can transform academic medical centers by analyzing clinical data, allowing healthcare providers to tailor management plans to individual characteristics. This approach has the potential to improve patient outcomes and optimization of resources, making it a necessary change especially for low middle income countries (LMIC) [ 3 ]. Academic leaders can create an action plan to effectively handle the changes caused by AI integration [ 4 ]. Several change management theories focus on the inner, extrinsic and social support factors as facilitators of the change process. Kurt Lewin’s three-stage model, ‘A systems model of change’, ‘the theory of change’, ‘the ADKAR model’, and ‘Kotter’s eight-step approach’ all offer ways for supporting change, from preparation to embedding change in organizational culture [ 5 , 6 ]. These models of change primarily emphasize structure and processes, with diminished attention to emotional and cognitive transformation. The SWITCH model of change, developed by Heath and Heath, simplifies dealing with change resistance by emphasizing positive features, goal clarity, and environmental and emotional support. This framework integrates both rational and emotional aspects, making it applicable in various contexts, from personal change to large-scale organizational transformations [ 7 ]. Because AI fosters change through a granular participatory approach, Lewin’s approach is frequently too simplistic. It sees change as a condition that may be attained and then frozen. The process of managing change for the integration of AI in healthcare facilities is ongoing. Scalable AI adoption requires that the SWITCH methodology change the emphasis from a destination to a method of working. As for the Kotter’s change model, it emphasizes a top-down approach and the creation of urgency. In situations where staff feel overwhelmed by ongoing innovations, increasing urgency can be counterproductive, resulting in employee burnout. The SWITCH model of change emphasizes on strengthening the human resource to address the emotional challenges of AI change, providing a psychological safety net that is often overlooked in top-down models, contrasting with Kotter’s vision centric approach. The ADKAR model omits the shape the path aspect. Knowledge alone is insufficient for this type of change; if the data is unstructured or the tools are complex. SWITCH tackles the issue by making the new behavior the path of least resistance. Additionally, the SWITCH model of change promotes rapid testing and adaption, which makes it perfect for integrating AI in healthcare settings where actual change at the individual and team level is required rather than merely creating a high-level organizational change plan. Because of its practical, behavior-focused, and flexible approach, the SWITCH model of change was selected for this study above other theories of change. Significant efforts have been made to develop AI solutions for healthcare improvement, yet healthcare professionals, particularly in LMICs, face challenges in implementing AI in their daily practice. Existing literature primarily emphasizes the technical performance, validation and accuracy of AI tools, while less focus is given to their integration into clinical service workflows. Change frameworks are often applied retrospectively or theoretically, lacking educationally informed and participatory processes. Most existing literature on AI in healthcare is conceptual or policy-oriented mostly from the developed countries. Pakistan’s AI ecosystem in healthcare has distinct structural, political, infrastructural, and socio-cultural constraints that are under-represented in the literature. How AI is experienced, negotiated, and implemented by healthcare professionals in developing countries remains insufficiently understood. This study used a multidisciplinary exploratory educational engagement strategy to support AI integration, where findings from earlier phases informed subsequent activities. Although each activity involved different participants, the sequential learning process allowed insights to accumulate and guide the development of ways of integrating AI in healthcare. The objective of the current study was to develop an actionable plan for integration of AI within an academic medical center [the Aga Khan University (AKU) and Aga Khan University Hospital (AKUH) Pakistan] using insights from interviews and workshop, structured according to SWITCH model of change. AKUH is a tertiary care hospital affiliated with academic entities (the Medical College and School of Nursing and Midwifery), and an Academic Medical Centre in Pakistan with an IQRA-based vision that prioritizes Impact, Quality, Relevance, and Access to health care [ 8 ]. It is known for its culture of innovation and leadership, as well as its commitment to consistently improving patient care standards and involving the community through outreach programs to raise health awareness and reduce disease burden [ 9 ]. Methods Study design and phases This exploratory qualitative study employed reflexive thematic analysis of semi-structured interviews and a co-design workshop, followed by the collaborative development of an action plan. The current study was conducted in three phases. Phase 1 involved individual semi-structured interviews with institutional leaders and faculty. Phase 2 consisted of a co-design workshop with multidisciplinary participants, utilizing the SWITCH framework as a sensitizing lens. Phase 2 focused on the collaborative development of a SWITCH-aligned action plan, synthesized from the findings of Phases 1 and 2. Ethical considerations Before commencement of the study permission from Aga Khan University’s Ethical Review Committee (ERC number 2023-8372-24124) was sought. Written informed consent was taken from all participants. Data collection was completed within the ERC-approved period (before March 2024). Participants and data collection The study was conducted from May 2023 to May 2024. Subsequent data cleaning and statistical analysis were conducted from March 2024 onward. Data were collected from various sources and participants via interviews, an interactive co-design workshop, and discussions, ensuring informed consent was obtained from all participants. The composition of participants varied across these Purposive sampling was done to gather data from various educational leaders [associate deans, departmental chairs, section heads, education leads of Postgraduate Medical Education (PGME) and Undergraduate Medical Education (UGME) and multidisciplinary faculty, clinicians] and UGME students, PGME students at AKU and AKUH [ 10 ]. The study’s objectives and the absence of conflicts of interest were communicated to all the participants. Considering the study’s objective, the sample of interest, the theoretical perspectives of the study, and the purposive sample selection, a sample of 10–12 participants was estimated for the interviews. However, data collection through interviews continued until thematic saturation was achieved. Workshop included 22 participants with almost negligible overlap with interview participants. Subsequent meetings for action plan development primarily involved the research team, and authors to refine and validate emerging insights. Prior to the interviews, a pilot study was conducted with two participants to evaluate the interview schedule’s effectiveness, leading to refinements in language and style. Interviewees were contacted through invitation emails and follow-up phone calls to schedule interviews. Semi-structured interviews, lasting 20–30 minutes, were conducted by trained research colleagues with qualitative research experience who also participated in pilot interviews prior to data collection. The research team (n = 4) included both men and women who used interview schedules, based on literature and with clear prompts, to carry out the process. The researchers framed the interviews as discussion sessions rather than performance evaluations to encourage open dialogue and reduce participant anxiety. Face-to-face interviews were audio recorded and thematically analyzed by the research team and the principal investigator. Clarifications were sought during and post-interviews to ensure accuracy, with key findings reviewed in team meetings. Using interview findings, a workshop aimed to discuss the actionable plan for integrating AI into healthcare. The workshop gathered collaborative data focused on how the SWITCH model of change can be utilized to create a comprehensive framework for AI integration in medical education and in-service practices at an academic medical center. A qualitative validation approach was implemented during the workshop, where participants discussed preliminary themes and findings, offering feedback. To mitigate social desirability bias, confidentiality of all participants was ensured, discussions were held neutrally, and open-ended questions were utilized to foster honesty. Researchers also considered their own biases and experiences to limit their impact on participants, while data triangulation was employed to enhance credibility. This process facilitated the triangulation of perspectives, ensuring that the findings accurately reflected the participants’ viewpoints. An action plan was developed and subjected to rigorous review through closed meetings, allowing for collaborator feedback and refinement to enhance effectiveness and feasibility. The plan was reviewed among authors via meetings and email. All interviews, workshop sessions, and planning meetings were audio recorded, transcribed verbatim, and anonymized. Data management was conducted manually using Microsoft Word and Excel. Data analysis Reflexive thematic analysis was conducted using Braun and Clarke’s six-step framework with slight modifications to suit the objective of the study [ 11 ], While steps 1–4 followed a systematic, interview based thematic analysis, steps 5 and 6 transitioned into participatory validation through a workshop and the subsequent development of an action plan for change. Microsoft Word and Excel were used to organize and analyze the notes and transcripts from interviews and workshop. As described in Fig 1 , the analysis began with an immersion phase, during which research team reviewed the transcripts several times to achieve thorough familiarity with the data. During this step, preliminary observations and reflexive notes were documented to identify initial patterns relevant to the study objectives. As step two of the process, the transcripts were divided into smaller units and initially coded by two researchers. Segments of data were labelled to capture meaningful patterns. The team held regular discussions to address coding discrepancies and presented data with illustrative quotes from participants, with their informed written consent. To ensure rigor in this process, the research team collaborated and resolved discrepancies through consensus, thereby enhancing inter-coder agreement. As a third step the codes were grouped into higher level categories to form overarching themes, with the research team reviewing the process for recurring patterns and key concepts. An inductive approach allowed themes to emerge from the data while acknowledging the researchers’ interpretative role. The research team mitigated potential bias by grounding the synthesis in participant accounts and reflective coding notes. All records of coding decisions, modifications and rationales were documented. As fourth step, the themes were iteratively reviewed, raw data was revisited and refined to ensure internal homogeneity (all data within a specific theme supported a unified interpretation) and external heterogeneity (ensuring themes were distinct). In the fifth step of the process, specific themes were established and further refined during a multidisciplinary workshop. This refinement aimed to ensure that the themes were both coherent and distinct from one another. Additionally, the workshop participants compared the insights and findings generated during their discussions with the themes identified in the interviews, ensuring a comprehensive evaluation of the data collected from multiple sources. Triangulation was achieved by comparing interview insights with data collected during workshops, capturing multiple perspectives and ensuring consistency across different settings. The use of collaborative problem solving and anonymity further validated the findings by reducing bias and confirming results directly with participants. Finally, as last step, thematic mapping and reporting were conducted, where the validated themes were aligned with the SWITCH model of change. The analytic integration utilized data source triangulation to synthesize interviews, workshop, and meetings into a cohesive action plan. To mitigate potential power imbalances, participants were guaranteed anonymity, ensuring that individual identities remained disconnected from the data. Potential bias in the analysis phase was managed through data source triangulation and the use of reflective notes to bracket pre-existing assumptions. By reconciling individual interview themes with broader workshop discourse and institutional planning data, the team ensured the final SWITCH-based framework was grounded in collective evidence rather than researchers led narratives. Findings were compared for thematic complementarity and subsequently reconciled through a rigorous process based on viability, stakeholder relevance, and institutional alignment. This iterative validation process ensured that the final SWITCH based action plan was not merely a thematic summary, but a strategically calibrated system designed to shape the path of AI change while addressing the documented elephant resistance of the workforce. By categorizing the emergent themes under the domains of directing the rider, motivating the elephant, and shaping the path, the analysis moved from descriptive findings to a structured framework for guiding AI related institutional change. Fig 1. Conceptual framework for ai integration in academic medical centers: Study phases and thematic analysis with integrated quality check measures. Open in a new tab Results Interview findings A total of eighteen interviews were conducted, representing professionals from diverse departments including Pathology and Laboratory Medicine, Women and Child Health, Dean’s Office, Hospital Operations, Department for Educational Development, Emergency Medicine, Pediatrics, Community Health Sciences, Surgery, School of Nursing, Quality Network of Quality, Teaching, and Learning (QTL_Net) and the Medical College of AKU. The male-to-female ratio among the participants was approximately 1.25 to 1. The range of years of experience among the professionals varied from 9–32 years, with a mean experience of 14.5 ± 5.1 years. Content analysis revealed following overarching themes: the potential of AI opportunities, resistance toward AI-induced changes, maximizing learning development and resources in AI to drive transformation through leadership styles, and importance of trainings/ capacity building to sustain change ( Table 1 ). Table 1. Leadership insights from structured interviews: themes, categories, and codes. Themes or Overarching Ideas Patterned Meaning or Categories Indicative Codes or Data Labels Opportunities for improvement Perceived benefits of AI anchored in present day clinical practice Knowledgebase of AI in the institute How they position themselves in relation to AI related changes Status of AI application in the institute Availability of AI-tools Optimistic imaginaries to implement AI at their healthcare institution Advantages of AI in research Advantages of AI usage in service Advantages of AI usage in education Limitations of current healthcare practices Improved patients’ outcome Reluctance to Embrace Change Personnel risk of AI integration related to roles, responsibilities and accountability Receptibility of stakeholders Job insecurity, professional uncertainty and overreliance on AI over healthcare professionals Knowledge gaps Traceability of wrong clinical decisions Challenges faced in adopting AI by users, strategic fragmentation Ethical concerns Lack of connect between AI ambitions in the institute and availability of technical, educational and financial resources Limitations of current AI-tools Financial challenges in approving use of AI Supply challenges in approving use of AI Lack of AI experts Enhancing Learning and Growth Active willingness in identifying educational gaps and providing resources to build AI competence for effective practice Educational reforms needed identifying AI as a core professional skill Resources for teaching and learning about the utility of AI in the institute for AI upskilling Hands on courses and research to enhance AI skills to improve organizational readiness to embrace this change Leadership Styles and Practices Need for formal guidance and consistent AI integration strategies Suggestions on how to implement AI in practice Identified need and readiness to develop concrete actionable plan to adopt AI Collective organizational strategies to compensate for limited resources AI advocates and AI enthusiasts required Interdepartmental collaboration and resource sharing AI Leadership to ensure safe AI practices Visionary leaders, unified strategic goals Overcoming change obstacles Continuous assessment to sustain change management Open in a new tab AI opportunities for improvement The interviewees framed AI not only as a technological upgrade of healthcare systems but as a performance enabling method for healthcare professionals. Predictive analytics and integrated AI-powered diagnostics were frequently mentioned by participants as becoming standard in clinical workflows to address labor shortages and improving efficiency. Several participants expressed enthusiasm about integrating AI into their administrative responsibilities to streamline hospital operations including providing care to patients at the bedside. Furthermore, responses from participants illustrated a wider awareness among healthcare professionals that AI solutions could be used for patient interaction, data flow, and patient management in addition to acting as channels for service delivery. This could indicate a change in hospital operations and professional duties within the healthcare system by shifting responsibility from hospital workers to digital interfaces. The AI tools were viewed as facilitators of timely and informed clinical decision making through rapid access to patient data and decision support systems. As one respondent (P16) noted, “AI finds utilization in triage in emergency medicine for better patient outcomes ” highlighting the role of AI in strengthening patient flow and time management in emergency settings. Participants envisioned an integrated healthcare system enabling patients to connect handheld devices to hospital networks for instant transmission and analysis of medical data, promoting individual care and overcoming cognitive limits. One of the participants (P12) remarked, “integrated healthcare system may not only enhance convenience for patients but also ensure quicker and more accurate decision-making by healthcare professionals, ultimately leading to improved patient outcomes.” Participants accounts suggested that AI use shifts the clinical decision making from reliance on individual judgement alone to a more data supported and system assisted process. A significant tension emerged between the initial expectations of AI as a productivity facilitator and the emergent risks of clinical dependence. Some also expressed apprehensions that increased dependence on AI may lead to over reliance to automated systems raising concerns about bias in clinical decision making. While participants anticipated that AI would alleviate cognitive load, they simultaneously expressed a drift toward automation bias, where over reliance on algorithmic outputs threatened to undermine individual clinical accountability and lead to the deskilling of the medical workforce. Participants further indicated that benefits of AI use could extend across many clinical departments. They associated AI related transformation with improvements in diagnostics across many disciplines. As one respondent (P2) explained “ AI is poised to advance in diagnostics, leveraging image analysis in pathology, radiology, enabled by rapid digitization of glass slides, mass spectrometry chromatograms and digital imaging technology” suggesting that integration of AI and diagnostic technologies are perceived as important drivers for accurate and timely patient management. One of the respondents (P5) associated AI related advancements is reshaping expectations in diagnostic laboratories, “in a laboratory setting, natural intelligence is utilized for analyzing organism colony morphology, biochemical reactions, microscopy, and susceptibility patterns based on previous experiences. my expectation with AI is going to be much higher than what I deliver right now and what my seasoned technologists deliver right now.” The participants responses indicated that they expect AI to handle tedious tasks, complex clinical data exceeding current performance standards thus raising their expectations significantly compared to current human capabilities. Across the interviews, participants highlighted the potential of AI tools to personalize learning pathways for medical students automate grading tasks with real-time feedback, paper setting and exams scoring. Additional uses include allowing students to set their own schedules and advance at different rates. This implied a more comprehensive rethinking of physicians as learning facilitators, interpreters of AI-generated insights, and not just content providers. As AI takes over the role of content provider by allowing students to advance at their own rates, the physician’s role may shift toward learning facilitation and the interpretation of AI generated insights. This represents a sociotechnical tension where the opportunity for student autonomy creates an existential challenge for clinicians, who need to redefine their professional value beyond their traditional expertise. Reluctance to embrace change The healthcare providers’ accounts of challenges linked to AI integration in their hospital highlighted both structural and psychological dimensions of organizational change. The participants consistently identified that any attempt at implementing AI would be met with resistance due to the fear of losing their job. Some participants expressed that AI being a new subspecialty would create jobs, but a significant hurdle would be acceptability. Concerns regarding job security highlight the tension between AI as an innovation and its potential threat to professional identity, revealing occupational anxiety and uncertainty about workforce stability and responsibilities in hospitals. Some participants framed AI as an enhancer of professional skills while maintaining job security, viewing its inclusion in healthcare systems as a complement to their roles rather than a substitute. As one participant (P14) explained, “AI was never meant to and is never meant to replace people” highlighting the continued importance of judgement in clinical care. Participants also acknowledged the challenge of introducing a major organizational change, emphasizing the need for meticulous planning to effectively adopt AI technology. This necessitates ongoing communication to articulate needs, expectations, and seek expert guidance for AI implementation. While the institute’s checks and balances ensure organizational safety and integrity, they can also impede or halt beneficial changes. Concerns regarding data privacy, patient consent, and cybersecurity were highlighted as both regulatory and moral obligations in the context of AI integration in hospitals. They were hesitant to fully endorse AI as a tool in medical education due to unresolved ethical issues and inadequate regulatory oversight. Participants’ acceptance coexisted with mild reservations, indicating that organizational, psychological, and ethical concerns had just as big of an impact on AI adoption as functionality and apprehension about employing AI technologies in healthcare. Participants identified the import of AI technology as a significant barrier to change, particular in a country like Pakistan where reliance on imported technology increases procurement complexity and cost. As a result, AI integration is a resource-intensive shift in which the availability of AI tools, cost, and infrastructure are critical to driving AI integration into healthcare. The participants highlighted concerns regarding reliance on imported AI solutions noting limited local development capacity as a potential barrier to sustainable implementation. As one respondent (P3) stated, “all our lab consumables even Eppendorf are imported, implying that AI tools are costly to import, and overall, AI integration costs make it difficult to sustain. If Pakistan had its own local AI systems for hospitals, we wouldn’t worry about import costs and integration would be sustainable.” Locally developed AI systems could help overcome barriers associated with high cost and import dependency. Interviewees expressed concerns regarding the importance of ensuring that imported AI solutions are matched to local needs, are validated on clinical data and conform to all relevant regulations. As one respondent (P9) noted, “I have seen lots of AI algorithms lack the evidence needed to support implementation. Clinicians will have to demand substantial resources either from the institute or the vendor to validate these tools in our local population ” highlighting the need for local validations, extra work and time and stronger support from leadership. Another participant (P2) stated “ AI makes diagnostics faster but makes the doctor’s day slower because of the validation burden ”. The findings reveal a contradictory drift where the opportunity for efficiency was not viewed as a simple benefit, but as a catalyst for anxiety regarding job security and the potential de-skilling of the clinical workforce. In the domain of diagnostics, a sharp contradiction emerged between the technical speed of AI tools and the temporal reality of clinical practice. While participants identified the opportunity for quick diagnostic interpretations, this was overshadowed by a validation burden. This created a drift from efficiency to increased workload, as clinicians reported that the cognitive effort of auditing an uninterpretable AI would exceed the time required for a traditional manual diagnosis. This matters because it suggests that AI does not simply save time but rather reconfigures clinical workload, shifting the burden from task execution to high-stakes oversight, which can lead to increased mental fatigue and burnout. Leadership styles and practices Participants emphasized that the extent of AI integration hinges upon the vision of its leaders. Leaders possess the ability to empower and facilitate AI-related change within the institution. To achieve this, they themselves must first acquire a comprehensive understanding of AI-based healthcare solutions and subsequently endeavor to maximize the benefits while mitigating associated challenges. Effective leadership entails advocating for change in a manner that alleviates fears and encourages acceptance of AI technology among others. Strong leadership to handle this change with robust IT security, are essential for the successful integration of AI in healthcare stated another respondent (P13), “Strong AI leaders in our hospital can reframe its use from just speed and efficiency to accountability and reduction in clinical errors” . The role of AI integration in hospitals as a real-time auditing mechanism was discussed. One of the respondents (P9) stated, “leadership should clearly communicate that AI transition is non-negotiable and as a tool for patient safety and transparency” thereby emphasizing that the transition towards AI systems enhances leadership oversight and transparency through the availability of traceable audit trails and real time monitoring of clinical activities. Leadership for AI integration in hospitals is strategic and relational rather than just administrative. The capacity of the leader to incorporate AI into the current workflow, cultivate trust, and reaffirm the crucial role of human health care workers in addition to AI tools will determine if the transition is effective. Participants varied opinions regarding leader’s approach to change management. Some preferred a more authoritative stance, while others advocated for a collaborative approach, involving users and stakeholders in planning and execution. Some also rationalized that AI-leadership can also emerge from individuals at lower levels who are passionate about the change and committed to supporting and guiding others. Like one participant (P11) stated , “a tech-savvy AI leader must be found in the institution, who does not have to be a departmental or institutional head and may be anyone as long as they are knowledgeable and enthusiastic in driving this change,” suggesting that expertise and AI competence may play a more important role than hierarchical position in leading such a massive project. A significant hierarchical drift emerged, where participants identified a shift from top-down management to distributed leadership. Technical proficiency, often found among junior staff or frontline clinicians, superseded traditional seniority. This creates a sociotechnical tension where the expert is no longer defined by years of clinical experience, but by their ability to navigate and implement AI tools, effectively decentralizing power within the hospital structure. Regardless of the approach, participants stressed that team building and strategic planning were crucial components for effective AI integration. Putting together committed cross-functional teams with people with a range of leadership philosophies, operational expertise, and clinical and AI understanding is essential. Forming a team is an effective organizational strategy to integrate AI practices, promote group ownership, and moderate possible resistance to change. Capacity building of healthcare providers and students Based on the interview data, limited infrastructure and shortage of AI-specialized professionals were the areas identified for attention. Participants consistently underlined that several training exercises prior to implementation were necessary to acquaint stakeholders and everyone else with AI to reduce errors, foster trust and confidence and clarify shifting roles brought by AI tools. According to the responses, the success of integrating AI into healthcare would also depend on trained personnel. It is crucial to have instructors or hospital officials who are knowledgeable about AI technology as role models and to instruct peers and pupils. Besides trainings, concerns about ethical issues prompt calls for regulatory policies, procedures and rigorous validation of AI tools to ensure reliability, patient safety and efficacy prior to use. Once formulated all involved (medical teachers and students) must be aware and practice these ethics in AI related regulations and policies. Validation of AI tools was framed not only as technical prerequisite but a confidence building mechanism to allowing healthcare providers to use AI tools with reduced fear of errors. Participants identified how AI integration can be a threat to patient care if healthcare providers are replaced with non-validated AI tools. One participant (P14) stated that “validating AI algorithms is crucial. Many overlook its importance and ethical implications for patients” highlighting concerns about accountability and responsible implementation. Similarly, another participant (P5) emphasized that “AI integration can jeopardize patient care if we gatekeepers are replaced with non-validated automated solutions” underscoring the importance of clinical validation and regulatory oversight prior to clinical adoption. Hence, the staff must be aware of AI tools validation processes and policies before implementing it. A significant thematic drift emerged within capacity building, where the focus expanded from individual technical proficiency to institutional policy literacy. Participants identified building capacity as a process of understanding the regulatory and ethical boundaries of AI use, shifting the clinician’s role from a simple end-user to a policy aware practitioner. This creates a tension between the desire for rapid innovation and the necessity for procedural compliance, where the speed of AI adoption is naturally constrained by the workload of policy validation. Participants assessed financial resources as necessary not just for acquiring AI technologies and an effective integrated clinical database, but also for sustaining training validation and system maintenance, in addition to cyber protection. Participants noted that AI algorithms require clean, curated clinical data. The Electronic Health Record (EHR) offers hope by centralizing medical records and data, improving accessibility and efficiency. Participants identified gaps in technical expertise and infrastructure as barriers to AI integration in healthcare. As one of the participants (P4) stated; “there is a deficiency of expertise, resources, and finances. Achieving progress requires investment in human resources and systems like EHR,” reflecting concerns of readiness of institutions to support this change. Workshop findings A workshop to strategize AI implementation in healthcare was held at AKU in collaboration with the Quality Network of Quality, Teaching, and Learning (QTL_Net) AKU. A diverse group of 22 individuals, including research analysts, faculty, PhD students, and undergraduate and postgraduate medical students from various departments (information technology, internal medicine, oncology, pathology, radiology, and psychiatry) participated in the workshop. Table 2 describes the collected data from this workshop concerning the identification of SWITCH components. Table 2. SWITCH components for change management to integrate AI in education, service and research in a hospital identified by workshop participants. Direct the Rider Motivate the Elephant Shape the Path Find Bright Spots ● Seeking guidance from local and international AI experts ● Find successful AI projects locally ● Identify departments where AI adoption led to improved outcome ● Showcase AI-projects. ● Recognize AI-enthusiasts with impact Find the Feeling ● AI Advocacy ● Engaging leadership. ● Justification for AI investments ● Financial feasibility of AI projects ● Regular evaluation of AI projects and action plan Tweak the Environment ● Collaborate on AI-initiatives. ● Allocating resources for AI-infrastructure. ● Align with organizational goals Script Critical Moves ● Integrating AI concepts into the curriculum ● Prototyping AI solutions ● Establishing an ethical AI Policy across education, research, and service Shrink Change ● AI Tools Availability ● Provide AI skills ● Adress resistance to change AI related clinical practices Build Habits ● AI training programs ● Integrate AI into workflows ● Encourage reflection on AI usage ● Establish accountability for AI practices ● Recognize AI enthusiasts Point to Destination ● Prioritizing AI applications. ● Setting goal of implementing AI within the healthcare setting ● Validate AI algorithms locally Grow Your People ● AI taskforce/ committee to lead the change ● Encouraging continuous learning related to AI ● Encourage AI innovations. ● AI Rewards sAI Awards Rally the Herd ● Leadership engagement & commitment ● Communicate AI action plan ● AI trainings at all levels ● AI community building ● Identify AI change agents. ● Celebrate small wins Open in a new tab The Rider represents the analytical and logical side of thinking, focusing on planning and execution. The Elephant represents the emotional and instinctive side of thinking, focusing on motivation and engagement. Each of these points addresses aspects of motivation, engagement, and emotional buy-in necessary for successful change management. the “Path” represents the environmental or situational factors that influence behavior change. It involves tweaking the environment to make the desired behavior easier and more likely to occur. The action plan for institutional change An analytical framework that captures how healthcare providers think about, negotiate, and react to AI in their current situations was developed from participant narratives. The proposed action plan conceptualized AI integration as multidimensional change process comprising of three domains: strategic actions for clinicians and healthcare providers (Direct the Rider), change pathway enablement and motivation and attitude (Motivate the Elephant) and environmental readiness (Shape the Path). The resulting action plan represents an analytic synthesis of the qualitative themes identified in Phase I. The action plan represents an analytic synthesis of the primary study themes, including opportunities for improvement, reluctance to change, leadership styles, and capacity building. By mapping these emergent findings onto the SWITCH framework, the plan moves beyond a generic policy guideline to provide a contextually grounded roadmap for institutional change. The proposed action plan for incorporating AI into research, education, and services at an academic healthcare facility is as follows: Direct the rider Engagement with AI Experts: To direct the rider, the action plan includes engagement with AI experts to provide the technical clarity and strategic guidance identified as necessary during the interviews. This action is an analytic synthesis of the capacity building theme, addressing the knowledge gaps reported by participants regarding AI integration and ethical governance. The need for multidisciplinary collaborations and the engagement of AI experts is highlighted by the fact that clinical expertise alone is insufficient to address the technical, ethical, and infrastructural aspects of AI-healthcare systems. AI experts can offer important guidance and expertise in implementing AI solutions, assisting in the successful navigation of complex technologies and methodologies. Collaborating with interdisciplinary teams, local and external AI experts, and individuals with experience ensures the accuracy, reliability, and safety of AI-driven healthcare solutions, leveraging diverse perspectives and resources for successful implementation. Curriculum Integration : The analysis highlights how a thorough medical curriculum (undergraduate and graduate) that covers both theoretical knowledge and real-world applications in healthcare settings must incorporate AI concepts and skills. To direct the rider, the plan proposes curricular reforms as a primary strategy for capacity building. This action is an analytic synthesis of participant concerns regarding the current lack of technical readiness. Specialized AI-related courses and continuous professional development (CPD) for physicians, supervisors, and teachers were identified as crucial enablers in enhancing both foundational knowledge and practical skills in the application of AI in healthcare. Capacity building and training in essential AI concepts such as machine learning, deep learning, and neural networks are emphasized as fundamental to the integration of AI in healthcare. These concepts support various applications including diagnostics, personalized medicine, predictive analytics, data management, medical image analysis, and clinical decision support systems. By integrating AI competencies into the formal curriculum, the framework ensures that future healthcare professionals possess the foundational knowledge required for ethical and effective AI utilization. Ethical, Legal, and Regulatory Considerations : By providing concrete legal guidelines, the plan gives the ‘Rider’ the technical clarity needed to proceed with AI adoption without the barrier of ambiguity. This is an analytic synthesis of the institutional challenges identified by participants, specifically the uncertainty regarding data privacy and liability. The action plan emphasizes the importance of ethics as a critical domain in the planning process for bringing AI in healthcare. It highlights the necessity to discuss and address ethical issues, including patient privacy, data security, algorithmic bias, and accountability. The study reveals a significant gap between the concerns surrounding AI in healthcare and the actual needs of healthcare providers. It highlights the necessity for an inclusive policy framework aimed at standardizing operations and addressing the potential risks noted by study participants. Such a policy should focus on protecting both patients and healthcare professionals by promoting the responsible advancement and application of AI technologies, while also safeguarding privacy, autonomy, and data security. Adherence to ethical standards will also help uphold equity in healthcare delivery and comply with legal and regulatory requirements. Strategic Prioritization: The findings highlight a sequential approach to prioritizing AI applications within organizations. This includes assessing their impact, scalability, feasibility, and alignment with organizational goals. Additionally, fostering a supportive AI culture and focusing on outcomes rather than processes in strategic planning are emphasized as crucial elements of this approach. To operationalize this component there is value in leveraging existing AI platforms in the institute like Computerized Physician Order Entry (CPOE), AKU’s Center of Innovation and Medical Education (CIME), Total Laboratory Automation (TLA), Virtual Learning Environment (VLE) and the ongoing initiative of AKU of Electronic Health Record systems. This approach not only minimize redundancy but capitalize on staff familiarity. One such example is AI-supported simulators in CIME which are in current times enabling professionals to safely practice procedures using real-world scenarios, utilizing AI algorithms for training purposes. The integration of CIME with VLE can be used as a medium to certify and re-certify for credentialling of faculty and staff to provide safe care to the patients. The VLE can also be strategically strengthened to provide clinicians with access to teaching and learning resources, case studies, and examples of clinical best practices. These examples are presented as illustrative options, showing possibilities or approaches. This approach directly addresses the capacity building theme by providing the clear roadmap that institutional leaders often lack. By dividing projects into quick wins (high-impact, low-effort) and long-term goals, the framework gives staff the specific, measurable targets needed to track progress and stay accountable. Objective Setting and Measurement: Importance of clear evidence-based objectives ensures alignment between strategy and execution of AI integration in the healthcare institutes. Specifying measurable objectives along with key performance indicators (KPI) is crucial for the successful implementation of AI, as it allows for the development of realistic timelines and key milestones. Furthermore, it establishes clear metrics or KPIs for evaluating performance effectively and will strengthen governance. This is an analytic synthesis of the institutional challenges and capacity building themes, addressing the lack of measurable objectives found in the data. By defining specific technical and clinical targets, the framework provides the rider with the necessary clarity to evaluate success and maintain accountability. Pilot Programs and Prototyping: The action plan incorporates pilot projects as an essential stage for feasibility assessment. This is an analytic synthesis of the institutional challenges and capacity building themes, as it allows for the iterative testing of protocols and data collection instruments in a controlled setting. By identifying technical flaws or workflow deficiencies early, the pilot provides the ‘Rider’ with the empirical evidence needed to refine the plan before full-scale implementation. These pilot studies are intended to evaluate viability, gather feedback, and refine implementation plans. Suggested actions include integrating AI into existing initiatives like EHR, CPOE, and AI-supported simulators in CIME and VLE to enhance healthcare outcomes through targeted projects. Motivate the Elephant. Advocacy and Leadership Engagement: To motivate the elephant, the action plan emphasizes ‘making noise’ through consistent internal communication and the celebration of small wins. Analysis suggests that the engagement and support of local leadership play a critical role in motivating the staff and in sustainable AI integration in the institute. Leaders may analyze the potential impact of establishing a database of AI experts and enthusiasts on supporting and advancing AI projects within their institute Potential leverage points for coordinated efforts and collaboration among AI project participants are suggested by the idea of holding regular casual open mic events or meetups to promote conversations on AI projects. This directly addresses the reluctance to change identified in the qualitative findings. By increasing the visibility of early successes, the plan counters emotional resistance and builds a sense of institutional momentum and shared excitement, transforming AI from a perceived threat into a collective opportunity. Accessibility of AI Materials: Analysis indicated that the initiative of AI integration in healthcare is influenced by availability and accessibility of AI-related resources, including tools, software, textbooks, online courses, journals, workshops, research papers, and educational tools. Ensuring the immediate availability of user-friendly AI tools serves to motivate the elephant by reducing the intimidation factor often associated with new technology. this directly addresses the reluctance to change identified in the interview findings. This will reduce cognitive load, encourage curiosity and creativity, and fuel intrinsic motivation to explore AI concepts. Analysis of potential resource avenues to provide opportunities to the healthcare providers would be required. These may include local and global AI funding opportunities, government AI-initiatives, multi- disciplinary or multi-institute collaborative AI projects and educational AI projects which can be identified and shared across the institute. When tools are accessible and produce quick, helpful results, it builds professional confidence, shifting the perception of AI from an added burden to a valuable supportive partner. Cost-Benefit Analyses: Conducting thorough cost-benefit analyses is crucial for justifying AI investments and securing funding from stakeholders. Performing a cost-benefit evaluation serves to motivate the elephant by transforming AI from a financial risk into a strategic advantage. This comprehensive evaluation helps identify and mitigate potential risks, reassures stakeholders, and builds confidence in the investment decision. The tangible benefits of AI initiatives, such as improved patient outcomes or cost savings, are more likely to attract investment. Clearly communicating benefits like increased revenue and improved patient outcomes builds institutional confidence and mitigates the emotional fears related to budget reallocations and resistance to change. Change Management Strategies: For the effective sustainability of changes related to AI within healthcare facilities, it is crucial to maintain active communication with staff members. Transformation strategies are essential for motivation by creating a culture of psychological safety and shared vision. This involves not only articulating the vision for AI transformation but also sharing feedback, as well as presenting examples of both successful and unsuccessful AI implementations. Such transparency fosters an environment of trust and collaboration. The data indicate that managing resistance to AI integration in an institution, requires a comprehensive approach involving communication, understanding their concerns, engaging them in co-designing solutions, providing trainings to build trust and confidence. This includes transparent explanations of AI implementation’s benefits, regular updates, open forums, and motivational open mic sessions. This approach directly addresses the reluctance to change identified in the interviews. By using inclusive decision-making, the strategy transforms AI from an imposed requirement into a collective institutional mission, building the emotional buy in needed for long-term sustainability. Continuous Learning and Innovation: Based on the interviews, the identified reluctance to change stems from a perceived threat to professional identity and job security. To counter this, the institutes can adopt a continuous learning strategy. This approach includes giving the staff the time and resources to evolve their skills incrementally, keeping them informed about the latest AI developments in healthcare, applications and encouraging staff to share insights and ideas for AI innovation. Evaluation and Iterative Improvement: By shifting the focus to incremental progress, the institute can aim to settle the elephant’s anxiety regarding job displacement or technical inadequacy. Regularly evaluate the overall impact of AI in medical education and service delivery. However, To ensure the continuous learning strategy remains effective and the staff emotions remains motivated, project evaluations must shift from pass/fail inspections to developmental milestones. Regular evaluations can be conducted as coaching conversations rather than top-down reporting. Celebrate small wins: Celebrating small wins serves as a direct psychological solution to the specific barriers identified in the interviews. Institute leadership may consider acknowledging AI innovations, AI initiatives, individuals performing well with AI tools and take put time to celebrate their efforts. For sustaining motivation and momentum, small wins and successful AI projects can be recognized through regular AI rewards and awards. Successful AI projects with tangible impacts can be linked to ongoing professional evaluations and performance appraisals. This strategy builds psychological safety by rewarding the effort of continuous learning, ensuring staff feel supported rather than pressured during the transition. Shape the path. Capacity Building: A critical insight from the interviews was that many individuals felt “not competent enough” to meet new AI related demands. By providing targeted training and resources, the framework builds the emotional confidence and technical skill needed to shape the path of the staff, transforming “incompetence” into a new professional identity. The approach aims to enhance AI literacy and competency among stakeholders through comprehensive capacity-building initiatives, including workshops, seminars, and online resources. It provides education and training programs on AI concepts, tools, and applications, empowers staff at all levels, and offers hands-on experience with AI tools. Regular practice and experimentation reinforce learning and proficiency. Change agents within the organization are identified to champion AI adoption and drive change. The action plan shapes the path by converting identified barriers into environmental tweaks such as simplified processes or better support tools that make the right actions easier to perform. This structural alignment ensures that capacity building isn’t just a standalone activity, but it clears the path and empowers the people to move along it sustainably. Collaborative Learning Spaces: As data analysis has indicated, the successful implementation of AI in hospitals requires support from clinical staff, and collaborative development can positively influence the AI related transformation. The action plan operationalizes collaborative learning spaces as a structural intervention to mitigate the barriers identified during the interview phase, specifically the perceived lack of individual competence. In the SWITCH model, this shift fosters a sense of community ensuring that AI implementation is supported by mutual exchange rather than isolated effort. By connecting to the concept of visionary leadership, these environments serve as crucial venues for interdisciplinary problem-solving, where leaders focus on encouraging the generation of ideas rather than simply giving orders. Infrastructure and Resource Allocation: Common factors such as structured organizational medical health records, financial resources, and technical infrastructure may serve as foundational elements for effective AI integration in healthcare systems. It’s essential to assess and allocate resources for infrastructure upgrades, data management, and user-friendly AI tool acquisition, ensuring the environment is effectively supported for effective AI implementation by planning and assessing existing infrastructure needs. This alignment ensures that the action plan does not merely mandate change but provides the structural machinery that renders the desired strategic outcome the path of least resistance. Data analysis also revealed how an overabundance of AI tools and applications may make it challenging to use and maintain, especially when there isn’t enough capacity to handle everything efficiently. Therefore, ensure that AI infrastructure and resource selection is guided by strategic needs and local context. By embedding these technical assets into the organizational topography, the action plan mitigates the barriers of cognitive overload and data fragmentation that contributed to the baseline perception of individual incompetence. Organizational Alignment: The action plan establishes organizational alignment as the foundational catalyst for institutional change, directly addressing the interview subthemes of “strategic fragmentation” and “professional uncertainty”. The hospital board’s support for technologically advanced AI systems and the integration of AI in hospitals is one financial and political enabler. If the AI-related process changes and AI change management approach are important to the entire plan and support the hospital’s overarching objectives, they will be feasible and effective. Involve stakeholders and executives in AI projects to encourage creativity and alignment with corporate objectives. Prioritize the engagement of a variety of stakeholders in the planning and decision-making processes. Such engagement and active communication allow for nuanced understanding of local contexts, makes incorporation of AI into workflows simple with ownership, and enhances acceptability and adaptability. Risk Management and Compliance : As AI introduces speed and scale in healthcare, necessitating oversight from healthcare professionals to bridge the gap between AI and risk management as well as accountability. Human oversight is vital to respect healthcare professionals’ autonomy and minimize adverse consequences. Risk management provides the psychological safety needed to overcome the instinct to refuse change as discussed by the interviewees and workshop participants. By establishing clear ethical policies for AI application and legal compliance structures, the framework transforms AI from a perceived threat into a managed tool. This directly countering the sub-theme of “fear of the unknown” identified in the interviews. Developing robust risk management and compliance strategies is crucial to navigate the ethical, legal, and regulatory challenges associated with AI in healthcare. It is imperative to conduct comprehensive risk mitigation measures for each patient-involved AI project before implementation. Continuous Monitoring and Feedback: The action plan utilizes iterative feedback loops as a primary mechanism to shape the path of change, addressing the interview theme of barriers by replacing ambiguity with real time navigational clarity. The feedback channels function as essential psychological scaffolding reducing the anxiety of the unknown and fostering the confidence required for sustained AI adoption. Data analysis further suggests establishing accountability mechanisms and providing support structures to encourage commitment to AI integration. Recognizing and rewarding successful AI adoption can incentivize continued engagement and motivate others to adopt similar habits. For sustainability of AI related change in the healthcare facility active communication with the staff along with feedback, sharing of AI-success stories and AI-failures is essential. This continuous exchange of insights not only directs the rider toward more efficient critical moves but also rallies the herd by transforming individual successes into shared organizational knowledge, thereby ensuring the change remains both scalable and culturally embedded. Sustainability and Scalability: Healthcare regulatory and compliance controls are crucial for the effective deployment of AI in healthcare. Data analysis highlights the importance of ongoing education to enhance AI skills, ensuring successful integration and sustainability within healthcare sectors. Strategies for the sustainability and scalability of AI initiatives ought to focus on capacity building, knowledge transfer, long-term funding, and continuous stakeholder communication to address concerns and maintain enthusiasm. As identified in the interviews the leadership plays a key role by aligning technology with clinical practice, promoting ethical use, and securing resources to foster trust. Sustainability and scalability are achieved by embedding capacity building into the organizational culture, ensuring that the initial bottom-up successes of leaders become reproducible habits. By addressing the barriers of incompetence through a collaborative learning space, the action plan creates a self-sustaining environment where AI implementation can scale naturally across interdisciplinary teams. Discussion Interviews at a Pakistani academic medical center uncovered significant themes regarding the integration of AI in healthcare, emphasizing opportunities, resistance to change, leadership practices, and the importance of building AI-related capabilities. These findings were further explored in a workshop that aimed to incorporate the SWITCH change management model. This study enhances the existing literature on AI in healthcare by proposing an action plan that utilizes the SWITCH model to align strategic direction, execution capability, and behavioral readiness in AI operations. The SWITCH model action plan introduces a process-oriented approach, showcasing key factors influencing AI adoption. It elaborates on the interplay of critical factors, including behavioral, motivational, and change-management dimensions, alongside technological readiness, throughout the AI implementation process in academic medical centers. It moves beyond conceptual prescriptions by empirically grounding AI implementation within the everyday practices of healthcare stakeholders, a dimension underexplored in prior conceptual frameworks. The study collected insights from healthcare personnel through various methods, highlighting the dual role of AI as a progress driver and a source of concern. It also involved consultations with AI experts and addressed ethical, legal, and regulatory aspects to ensure safe and reliable AI solutions. A triangulated methodology, utilizing interviews and workshops, facilitated a comprehensive understanding, aiming to refine the action plan for successful AI integration in medical education and practice [ 10 , 11 ]. The action plan focuses on addressing the human and organizational facets of AI implementation in healthcare, recognizing that such implementation is still in its early stages and requires further exploration. A scoping review published in 2022 identified research on AI implementation frameworks in healthcare, focusing on publications since 2000 [ 12 ]. Out of 2541 screened articles, only seven met the eligibility criteria; two provided formal frameworks for AI implementation. New domains highlighted in the review included data reliance, existing processes, shared decision-making, human oversight, and ethical considerations concerning population impact and inequality. Although previous frameworks have identified barriers, enablers, and evaluation steps, they frequently do not provide comprehensive integration across various perspectives [ 13 – 21 ]. Rahim et al synthesized existing studies to identify enablers and barriers and mapped these to a three-horizon roadmap to guide hospitals toward becoming learning health systems with successful AI integration. This study complements existing systematic review by Rahim et al on AI-enabled learning health systems by translating identified enablers and barriers into a stakeholder-informed action plan [ 22 ]. While Nene and Hewitt propose a framework for implementing AI in South African public hospitals, grounded in existing literature and contextual constraints, our study extends this work through an empirical, stakeholder-driven approach translating conceptual considerations into actionable and learning-oriented practices [ 23 ]. Notably, the development of structured, stakeholder-informed AI frameworks is essential not only in resource-constrained environments but also in technologically advanced health systems. For example, digitally advanced health systems such as Australia are actively developing AI governance frameworks [ 24 ]. The registered protocol plans to combine a scoping literature review, document analysis, stakeholder interviews, and validation workshops to produce a governance model intended for a major Australian public health organization with multiple hospital sites highlighting the ongoing complexity and evolution of responsible AI implementation as a challenge. The practical, structured checklist FAIR-AI developed in United States, demonstrates that AI implementation should be treated like a clinical intervention and is a continuous clinical governance process not a one-time technical deployment [ 25 ]. While patient safety, audits of AI tools and their outcomes were identified as paramount in our study, FAIR-AI also detailed the need for an AI framework to evaluate the impact of potential solutions on health system. The FAIR-AI reinforces the need for learning-oriented, stakeholder-informed approaches like AI action plan developed in this study. In LMICs like Pakistan AI is accelerating but many hospitals are still moving cautiously. Multiple study participants cited how managerial skills and leadership insights are essential for bridging the gaps between AI and clinical practice and to expedite the change. As identified by current study and reported in published reviews, research studies and conceptual literature on AI, there is a need for robust managerial, ethical, and quality systems to ensure successful AI applications [ 26 – 31 ]. Healthcare regulatory and compliance controls must ensure AI-enabled solutions are safe and effective and promote a culture where AI failures are learned from [ 32 ]. Emotions may be conveyed throughout this implementation phase, so leaders must remain connected [ 33 ]. The challenges associated with AI related changes are manageable with effective communication from the beginning and beyond [ 34 ]. The action plan for integrating AI into healthcare demonstrates significant potential for improving operational efficiency and patient outcomes, setting a model for other institutions [ 35 , 36 ]. This study has certain limitations that should be considered: The current study was conducted in a single private hospital in Pakistan hence the findings may not be generalizable to other hospitals with different challenges and digital infrastructure. In this era of fast-moving AI advancements, the study may not reflect changing attitudes and perception of healthcare providers as we gathered data at one point in time. The data from the interviews mainly included leadership who may already be inclined towards this AI related transformation. To address this potential selection bias, we have tried to include participation from multidisciplinary faculty and various levels of students ensuring broader perspectives and balanced representation. We acknowledge the potential for insider bias given the lead researcher’s role in data collection and analysis. This study’s principal investigator along with the research team maintained dual roles as both investigators and active members of the studied institution. This proximity to the subject matter introduces the risk of social desirability bias; participants may have altered their responses either consciously or unconsciously to align with perceived institutional expectations or to maintain professional rapport with the researchers. The research team’s familiarity with the organization may have led them to overlook routine issues (blind spots) that an external observer would have identified as significant. To mitigate this, we tried to maintain analytical distance through reflexive self-review (principal investigator maintained a reflective diary throughout the study) and ensure that findings are grounded in the participants’ data. While being ‘insiders’ helped the research team understand the organization deeply, it may have affected the study’s objectivity. Furthermore, the use of an inductive analytic approach allowed for uncomfortable themes like the perceived lack of competence and fear of change to emerge organically, rather than being filtered out by institutional loyalty. Therefore, the findings represent a shared perspective of the institution rather than a completely neutral view. The reliance on the SWITCH framework may have biased the results by overlooking perspectives outside of that model. Additionally, because there was no external review or pilot testing of the final plan, its success and usefulness in different contexts are not yet proven. These factors limit the current credibility and applicability of the study’s conclusions. Consequently, these results should be viewed as an initial developmental stage. For the successful adaptation of the proposed action plan by other hospitals, local validation of its effectiveness is essential. It is crucial for these institutions to establish clear performance indicators, encompassing audit mechanisms and outcome measures. Furthermore, the implementation of healthcare AI in Pakistan should prioritize resource availability, capacity building, relevance to local clinical needs, and ethical governance. Future work should therefore focus on pilot implementation and empirical validation of the action plan across diverse healthcare settings to assess its feasibility, effectiveness, and scalability. Conclusion The study outlines a comprehensive strategy for integrating AI into healthcare, focusing on strategic change management, AI competency enhancement, effective leadership, continuous education, and ethical frameworks. It helps healthcare professionals understand the complexities of AI adoption. AI has the potential to improve healthcare delivery, patient outcomes, and address challenges in LMIC hospitals; however, it must be ethically developed, culturally sensitive, and aligned with LMIC healthcare needs in order to be sustainable. Acknowledgments Since the corresponding author was the recipient of the Aga Khan University’s QTL_Network’s Award for Collaborative Practice Scholarship of Teaching and Learning SoTL, the study was carried out with support from QTL_Net. 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PMC Copyright notice 7 Oct 2025 Evidence-Based Action Plan for Integrating Artificial Intelligence in an Academic Medical Centre-A Multidisciplinary Approach PLOS ONE Dear Dr. Jafri, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. To progress toward peer review, the manuscript must be revised to include a detailed and explicit discussion of study's limitations, potential biases, and the justification or calculation for the sample size. Furthermore, to comply with the journal's reporting standards, the COREQ checklist must be fully completed and submitted alongside the revised manuscript. Once these changes are made and the required checklist is provided, we will assess the manuscript's suitability for progression to the peer-review process. Please submit your revised manuscript by Nov 21 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at [email protected] . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. 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Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols .... We look forward to receiving your revised manuscript. Kind regards, Syed Hani Abidi Academic Editor PLOS ONE Journal Requirements: When submitting your revision, we need you to address these additional requirements. 1.Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf 2. 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If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise. [Note: HTML markup is below. Please do not edit.] [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/ . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at [email protected] . Please note that Supporting Information files do not need this step.. Please note that Supporting Information files do not need this step.. Please note that Supporting Information files do not need this step.. Please note that Supporting Information files do not need this step. PLoS One. 2026 Apr 17;21(4):e0346582. doi: 10.1371/journal.pone.0346582.r002 Author response to Decision Letter 1 Article notes Copyright and License information Collection date 2026. PMC Copyright notice 28 Oct 2025 October 2025 To the Editor-in-Chief PLOS ONE Subject: Rebuttal letter for article titled- “Evidence-Based Action Plan for Integrating Artificial Intelligence in an Academic Medical Centre-A Multidisciplinary Approach” Dear Sir, Thank you for your feedback and guidance. We have addressed all concerns raised point by point below. • Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. To progress toward peer review, the manuscript must be revised to include a detailed and explicit discussion of study's limitations, potential biases, and the justification or calculation for the sample size. Response: Study now includes limitations, potential biases, and the justification or calculation for the sample size. • Furthermore, to comply with the journal's reporting standards, the COREQ checklist must be fully completed and submitted alongside the revised manuscript. Once these changes are made and the required checklist is provided, we will assess the manuscript's suitability for progression to the peer-review process. Response: the COREQ checklist has been submitted now. • When submitting your revision, we need you to address these additional requirements. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf Response: We have revised the manuscript according to the PLOS ONE style requirements and ensured that all files are renamed and formatted in line with the provided templates including the title page. • We note that your Data Availability Statement is currently as follows: “All relevant data are within the manuscript and its Supporting Information files.” Please confirm at this time whether or not your submission contains all raw data required to replicate the results of your study. Authors must share the “minimal data set” for their submission. PLOS defines the minimal data set to consist of the data required to replicate all study findings reported in the article, as well as related metadata and methods ( https://journals.plos.org/plosone/s/data-availability#loc-minimal-data-set-definition ). For example, authors should submit the following data:- The values behind the means, standard deviations and other measures reported;- The values used to build graphs;- The points extracted from images for analysis. Authors do not need to submit their entire data set if only a portion of the data was used in the reported study. If your submission does not contain these data, please either upload them as Supporting Information files or deposit them to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. For a list of recommended repositories, please see https://journals.plos.org/plosone/s/recommended-repositories . If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially sensitive information, data are owned by a third-party organization, etc.) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent. If data are owned by a third party, please indicate how others may request data access. Response: We have already shared the themes, even codes and analysis of the qualitative data. The full data cannot be shared due to confidentiality restrictions. However all relevant data supporting the findings including coding, themes etc are described in the paper. The study can be replicated using the details provided in the manuscript. • If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise. Response: Not applicable Regards, Corresponding Author: Dr Lena Jafri Attachment Submitted filename: October 2025 point by cpoint response.docx pone.0346582.s001.docx (22.5KB, docx) PLoS One. doi: 10.1371/journal.pone.0346582.r003 Decision Letter 1 Syed Hani Abidi Syed Hani Abidi Academic Editor Find articles by Syed Hani Abidi Author information Copyright and License information Roles Syed Hani Abidi : Academic Editor © 2026 Syed Hani Abidi This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. PMC Copyright notice 26 Dec 2025 Dear Dr. Jafri, Specifically, the authors should revise the abstract to provide a concise scientific summary that includes a clear justification for the chosen methods, a brief description of the analytic procedures, and concrete, validated outcomes. Additionally, restructure the introduction to clearly articulate a specific research gap and validate the selection of the SWITCH model over alternative frameworks. In the methods section, adopt a coherent qualitative design by clearly defining whether you are using a grounded-theory approach or alternate method. Provide a justification for the prospective sample size and address aspects of researcher reflexivity, along with triangulation through explicit procedures. Finally, deepen the results and action plan sections by incorporating richer, analytically driven themes, including illustrative quotations and a validated, prioritized implementation framework. The discussion must also critically reflect on methodological limitations and ethical considerations around AI usage, power dynamics, structural constraints. [email protected] . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. A letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols .... We look forward to receiving your revised manuscript. Kind regards, Syed Hani Abidi Academic Editor PLOS One Journal Requirements: If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author Reviewer #1: (No Response) Reviewer #2: (No Response) ********** 2. Is the manuscript technically sound, and do the data support the conclusions??> Reviewer #1: Yes Reviewer #2: Partly ********** 3. Has the statistical analysis been performed appropriately and rigorously? -->?> Reviewer #1: Yes Reviewer #2: Yes ********** 4. Have the authors made all data underlying the findings in their manuscript fully available??> The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.--> Reviewer #1: Yes Reviewer #2: Yes ********** 5. Is the manuscript presented in an intelligible fashion and written in standard English??> Reviewer #1: No Reviewer #2: Yes ********** Reviewer #1: Abstract-The abstract is largely descriptive and reads more like a project summary than a scientific abstract. Critical elements are missing, including a clear justification for methodological choices, explicit analytic procedures, and concrete outcomes beyond thematic labels. Claims of providing a “structured strategic action plan” are not supported by evidence of validation, implementation, or evaluation. Introduction-The introduction provides a generic overview of AI in healthcare and change management theories but lacks a sharply articulated research gap. The rationale for selecting the SWITCH model over other established frameworks remains underdeveloped and largely asserted rather than justified. The novelty of the study is unclear, particularly given the abundance of conceptual papers on AI integration and leadership in healthcare. Methods-This is the weakest section of the manuscript. • The study is labeled as “qualitative cross-sectional,” yet the design blends interviews, workshops, and consensus meetings without a coherent qualitative methodological framework. • Claims of using grounded theory are not supported by methodological rigor (e.g., no constant comparison, no theory generation, no memoing). • Sample size justification is post-hoc and conceptually weak; reliance on a pilot of two interviews to justify adequacy is not defensible. • The role of researchers (many of whom are institutional insiders and co-authors) raises significant concerns regarding reflexivity, positionality, and social desirability bias, which are insufficiently addressed. • Triangulation is asserted but not demonstrated analytically. Results-The results are largely descriptive and repetitive. • Themes are broad, unsurprising, and closely aligned with existing literature, offering limited analytical depth. • Coding appears largely confirmatory, reinforcing pre-existing assumptions about AI rather than generating new insights. • Quotations are sparse and selectively illustrative rather than analytically rich. • Tables function more as checklists than as vehicles for theory building or interpretive synthesis. Action Plan (Phase III)-The action plan is extensive but essentially prescriptive and aspirational. • It resembles a policy or institutional guideline rather than a research output. • No prioritization framework, feasibility testing, stakeholder validation beyond authors, or implementation metrics are provided. • The plan is not empirically evaluated, piloted, or compared against alternatives, limiting its scholarly contribution. Discussion-The discussion reiterates results without critically engaging with methodological limitations or alternative interpretations. • Claims of scalability and transferability are speculative and not supported by comparative data. • The discussion conflates relevance with rigor; importance of topic does not compensate for methodological weakness. • Engagement with critical AI ethics, power dynamics, and structural constraints in LMIC contexts remains superficial. Limitation-Although a limitations section is present, it is largely formulaic and understated. • Key issues such as insider bias, lack of independent validation, and absence of outcome assessment are not adequately acknowledged. • The implications of these limitations for the credibility of the action plan are not discussed. Reviewer #2: The paper has a relevant and timely focus on AI inclusion in medical education and research, and action plan using the SWITCH model), which could be attractive if methodological rigor, clarity, and positioning are strengthened. The core issues now are precision and depth in the qualitative methodology, sharper articulation of novelty, and substantial tightening of language and structure. These will enhance the article and will benefit wider audience. ********** what does this mean? ). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? 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NAAS will assess whether your figures meet our technical requirements by comparing each figure against our figure specifications.<button aria-busy="false" aria-describedby="tooltip-750" class="button button-for-icon button-small button-ghost-weak lumo-no-copy" style="box-sizing: inherit; margin: 0px; padding: 0px; border-width: 1px; border-style: solid; border-color: rgba(0, 0, 0, 0); border-image: none 100% / 1 / 0 stretch; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial; font: inherit; color: rgb(12, 12, 20); appearance: none; cursor: pointer; vertical-align: middle; padding-block: 4.28571px; padding-inline: 4.28571px; border-radius: 8px; outline: unset; transition: 0.15s cubic-bezier(0.22, 1, 0.36, 1), background-position; --button-default-background-color: transparent; --button-hover-background-color: rgba(186, 190, 199, 0.2); --button-active-background-color: rgba(186, 190, 199, 0.3); --button-default-text-color: #0c0c14; --button-hover-text-color: #0c0c14; --button-active-text-color: #0c0c14; --padding-block: .3571428571em; --padding-inline: .3571428571em; quotes: "“" "”" "‘" "’"; min-block-size: 0px; min-inline-size: 0px; scrollbar-width: thin; scrollbar-color: rgba(0, 0, 0, 0) rgba(0, 0, 0, 0);" type="button"> </button> Attachment Submitted filename: PONE-D-25-46411R1.docx pone.0346582.s002.docx (15.7KB, docx) PLoS One. 2026 Apr 17;21(4):e0346582. doi: 10.1371/journal.pone.0346582.r004 Author response to Decision Letter 2 Article notes Copyright and License information Collection date 2026. PMC Copyright notice 23 Jan 2026 Jan 2026 To The Editor-in-Chief PLOS ONE Subject: Response to reviewers’ comments for article titled “Evidence-Based Action Plan for Integrating Artificial Intelligence in an Academic Medical Centre-A Multidisciplinary Approach” Dear Sir, We have revised the manuscript in accordance with the academic editor’s and reviewers’ suggestions. A point-by-point response is provided below, along with both a tracked-changes version and a clean revised manuscript submitted on the PLOS One portal. 1. Specifically, the authors should revise the abstract to provide a concise scientific summary that includes a clear justification for the chosen methods, a brief description of the analytic procedures, and concrete, validated outcomes. Additionally, restructure the introduction to clearly articulate a specific research gap and validate the selection of the SWITCH model over alternative frameworks. Response: Abstract has been revised as per suggestions. We clarify that the aim of this study was exploratory and developmental, focusing on the co-creation of a practical framework rather than the validation of outcomes. We have revised the manuscript to clarify the exploratory nature of the study, explicitly describe the analytic process, articulating a clearer research gap, and reason behind choosing SWITCH over other models of change for devising the action plan. 2. In the methods section, adopt a coherent qualitative design by clearly defining whether you are using a grounded-theory approach or alternate method. Provide a justification for the prospective sample size and address aspects of researcher reflexivity, along with triangulation through explicit procedures. Response: We agree with reviewers. In this study, the grounded theory approach has been removed in favor of an exploratory research design that used thematic analysis. This method aimed to uncover unknown patterns and themes from interview and workshop discussion data, employing an inductive approach to ensure findings were data-driven and allowed themes to emerge organically from participants' responses, aligning with the exploratory nature of the study. By employing the concepts of information power and theme saturation to clearly justify the anticipated sample size, we have now improved the Methods section. Additionally, we have included a clear reflexivity statement and described the triangulation techniques used, such as data source and analyst triangulation. 3. Finally, deepen the results and action plan sections by incorporating richer, analytically driven themes, including illustrative quotations and a validated, prioritized implementation framework. The discussion must also critically reflect on methodological limitations and ethical considerations around AI usage, power dynamics, structural constraints. Response: Results have been edited, added more quotations, tried writing it analytically with interpretations where needed. Since this was beyond the scope of the study, the action plan was not validated; however, the SWITCH model of change framework was employed, and the results were integrated into the SWITCH framework. The Discussion has been revised focusing on literature review, ethics and limitations of the study. 4. Reviewer #1: Abstract-The abstract is largely descriptive and reads more like a project summary than a scientific abstract. Critical elements are missing, including a clear justification for methodological choices, explicit analytic procedures, and concrete outcomes beyond thematic labels. Claims of providing a “structured strategic action plan” are not supported by evidence of validation, implementation, or evaluation. Response: The abstract has been revised as per these suggestions of writing it scientifically. We wish to clarify that the manuscript does not claim formal validation or implementation of the action plan. Rather, it presents a conceptual, structured action plan developed from qualitative findings and aligned with the SWITCH model, intended to guide institutions in AI integration. We have revised the text to make this distinction clearer and avoid any potential misunderstanding. 5. Introduction-The introduction provides a generic overview of AI in healthcare and change management theories but lacks a sharply articulated research gap. The rationale for selecting the SWITCH model over other established frameworks remains underdeveloped and largely asserted rather than justified. The novelty of the study is unclear, particularly given the abundance of conceptual papers on AI integration and leadership in healthcare. Response: The Introduction has been strengthened to clearly articulate the existing gaps, novelty and the rationale for conducting this study. The SWITCH model was selected for its focus on practical behaviour change among healthcare stakeholders, rather than just organizational restructuring. It uniquely incorporates cognitive, emotional, and environmental factors, suitable for the study's multi-phase design involving interviews, discussions, and workshops over six months. This flexibility allowed its application across various study stages and stakeholder groups. The manuscript has been revised to more clearly justify the selection of the SWITCH model (in the introduction). 6. Methods-This is the weakest section of the manuscript. The study is labeled as “qualitative cross-sectional,” yet the design blends interviews, workshops, and consensus meetings without a coherent qualitative methodological framework. Response: Thank you for this comment, we have revised the methods section and agree and the study design has been modified too. 7. Methods-Claims of using grounded theory are not supported by methodological rigor (e.g., no constant comparison, no theory generation, no memoing). Response: In this study, the grounded theory approach has been removed in favor of an exploratory research design that used thematic analysis. This method aimed to uncover unknown patterns and themes from interview and workshop discussion data, employing an inductive approach to ensure findings were data-driven and allowed themes to emerge organically from participants' responses, aligning with the exploratory nature of the study. We have revised the Methods section to remove references to grounded theory, as the study did not aim to generate a formal theory. 8. Methods-Sample size justification is post-hoc and conceptually weak; reliance on a pilot of two interviews to justify adequacy is not defensible. Response: We respectfully clarify that the sample size was not determined post hoc. The proposed sample size of interviews was specified a priori and approved by the institutional ethics review committee before data collection commenced. We agree, however, that our original explanation relied too heavily on the pilot interviews. We have therefore revised the Methods section to remove the pilot based justification and now explicitly ground the sample size rationale in established qualitative principles, including purposive sampling of information-rich participants and anticipated thematic saturation. 9. Methods-The role of researchers (many of whom are institutional insiders and co-authors) raises significant concerns regarding reflexivity, positionality, and social desirability bias, which are insufficiently addressed. Triangulation is asserted but not demonstrated analytically. Response: Agree, this has been addressed in the methods section now. We have updated the Methods section to specifically consider researcher positionality, including prior experience and institutional familiarity. Confidentiality was guaranteed to participants, and open-ended questions were used in neutral, non-evaluative interviews to promote candid observations. Throughout the process of gathering + analyzing data, researchers kept written reflections to track how their viewpoints might have affected interpretations. To increase credibility and lower the possibility of social desirability bias, results were also triangulated across participant views and cross-checked with pertinent documents whenever feasible. 10. Results-The results are largely descriptive and repetitive. Response: This was a helpful comment. We agree that earlier version of results were too descriptive. Results section has been revised by the authors and we have tried to remove repetition and tried to strengthen analytical depth. 11. Results-Themes are broad, unsurprising, and closely aligned with existing literature, offering limited analytical depth. Response: We recognize that multiple themes presented align with existing literature. The themes were developed inductively and later discussed with workshop and meeting participants. In response to the reviewer's critique, we have enhanced the Results section by providing analytic commentary within each theme, elucidating the interconnections among themes to enhance interpretive depth. 12. Results-Coding appears largely confirmatory, reinforcing pre-existing assumptions about AI rather than generating new insights. Response: We thank the reviewer for this comment. We respectfully clarify that the analysis was not intended to be confirmatory. Any alignment with existing literature would therefore have been unintentional. An inductive coding approach was employed, deriving codes from participants’ accounts instead of predefined theories. Acknowledging the initial presentation's shortcomings, the Methods and Results sections were revised to better articulate the inductive coding process and enhance the analytic commentary, emphasizing how participant perspectives influenced and expanded existing understandings of AI implementation. 13. Results-Quotations are sparse and selectively illustrative rather than analytically rich. Response: We have revised the Results section as per this helpful suggestion to ensure that quotations are used analytically rather than illustratively. Pre or post quotation analysis have been added where appropriate. More quotes have been added. In the initial version quotations were used selectively to avoid overly descriptive results. But now we understand how relevant quotations can be added. 14. Results-Tables function more as checklists than as vehicles for theory building or interpretive synthesis. Response: We agree with the reviewer and this is an important observation. We have tried to edit the tables as much as we could to strengthen this aspect of the paper. 15. Action Plan (Phase III)-The action plan is extensive but essentially prescriptive and aspirational. It resembles a policy or institutional guideline rather than a research output. Response: We appreciate the reviewer’s recognition of the action plan’s scope and practical relevance. The action plan was analytically derived which is not coming through. We have clarified its analytic intent to ensure it is read as a research output rather than a prescriptive guideline. 16. Action Plan (Phase III)-No prioritization framework, feasibility testing, stakeholder validation beyond authors, or implementation metrics are provided. The plan is not empirically evaluated, piloted, or compared against alternatives, limiting its scholarly contribution. Response: We appreciate the reviewer’s insightful critique and agree that empirical evaluation, feasibility testing, stakeholder validation and comparative analysis would strengthen the work. We wish to clarify that the primary aim of this manuscript is to propose a conceptual and integrative framework, rather than to report empirical findings. Please note, data collection (both in interviews and also in workshop) included many organizational leaders and decision-makers, who function as key stakeholders in the context of this study. This informally does provide stakeholder validation beyond the authorship team. 17. Discussion-The discussion reiterates results without critically engaging with methodological limitations or alternative interpretations. Claims of scalability and transferability are speculative and not supported by comparative data. The discussion conflates relevance with rigor; importance of topic does not compensate for methodological weakness. Engagement with critical AI ethics, power dynamics, and structural constraints in LMIC contexts remains superficial. Response: Discussion has been rewritten taking account of literature, limitations and avoiding repetition of results. Moving beyond a summary of results, we now critically evaluate our findings against existing literature. While the importance of the topic remains a driver, we have reinforced the study's rigor by being more transparent about our methodological choices and the validity of our conclusions. Please note the findings from the current study offer potential insights into how healthcare institutes may integrate AI based on study findings. These insights are exploratory and provide a foundation for future empirical and comparative studies. Therefore, claims about scalability and transferability are presented as potential insights rather than confirmed outcomes. Further studies with larger samples and comparative data are needed to validate these aspects. 18. Limitation-Although a limitations section is present, it is largely formulaic and understated. Key issues such as insider bias, lack of independent validation, and absence of outcome assessment are not adequately acknowledged. The implications of these limitations for the credibility of the action plan are not discussed. Response: Limitations section has been written and we have tried to address the potential biases, validations and outcome assessment need. 19. Reviewer #2: The paper has a relevant and timely focus on AI inclusion in medical education and research, and action plan using the SWITCH model), which could be attractive if methodological rigor, clarity, and positioning are strengthened. The core issues now are precision and depth in the qualitative methodology, sharper articulation of novelty, and substantial tightening of language and structure. These will enhance the article and will benefit wider audience. Response: Thank you for the comments. As per reviewers’ and editor’s suggestions methods and results have been modified bringing clarity, analytical interpretation and relevance. We have tried to improve writing and structure of the manuscript too. Attachment Submitted filename: Jan 2026 response to plos one reviewers SWITCH.docx pone.0346582.s003.docx (24.8KB, docx) PLoS One. doi: 10.1371/journal.pone.0346582.r005 Decision Letter 2 Syed Hani Abidi Syed Hani Abidi Academic Editor Find articles by Syed Hani Abidi Author information Copyright and License information Roles Syed Hani Abidi : Academic Editor © 2026 Syed Hani Abidi This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. PMC Copyright notice 10 Mar 2026 Dear Dr. Jafri, Please submit your revised manuscript by Apr 24 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at [email protected] . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. 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Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author Reviewer #1: (No Response) Reviewer #2: All comments have been addressed ********** 2. Is the manuscript technically sound, and do the data support the conclusions??> Reviewer #1: Yes Reviewer #2: Yes ********** 3. Has the statistical analysis been performed appropriately and rigorously? -->?> Reviewer #1: N/A Reviewer #2: Yes ********** 4. Have the authors made all data underlying the findings in their manuscript fully available??> The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.--> Reviewer #1: Yes Reviewer #2: Yes ********** 5. Is the manuscript presented in an intelligible fashion and written in standard English??> Reviewer #1: Yes Reviewer #2: No ********** Reviewer #1: The manuscript addresses a timely and contextually important issue; however, before it can be considered for publication, several substantive concerns require further attention. In particular, 1. The study should explicitly and consistently define its methodological orientation (e.g., exploratory qualitative study using reflexive thematic analysis) and ensure alignment between research questions, data collection methods (interviews, workshop, meetings), and analytic procedures. 2. Provide a clearer step-by-step account of how codes were generated, how themes were refined, and how decisions were made during analysis. A visual analytic map or thematic development table would enhance transparency. 3. Move beyond descriptive summaries of what participants said. For each theme, include interpretive commentary that explains why these patterns matter, how they relate to existing literature, and what tensions or contradictions emerged. 4. Integrate quotations analytically rather than illustratively. Each quote should be followed by interpretation that demonstrates how it advances conceptual understanding rather than merely exemplifying a theme. 5. Given the insider status of several authors, include a more explicit reflexivity statement detailing positionality, power dynamics, and how potential bias was managed throughout data collection and analysis. 6. Specify how interview findings, workshop discussions, and planning meetings were compared, reconciled, and synthesized into the final action plan. The analytic integration process requires clearer articulation. 7. Provide a more critical comparison with alternative change-management frameworks, explaining precisely what conceptual gap the SWITCH model fills in this context. 8. Clearly distinguish the action plan from a policy guideline by articulating the theoretical insights derived from the data. Consider presenting a prioritization matrix or conceptual model that demonstrates analytic synthesis rather than listing components. 9. Explicitly acknowledge insider bias, absence of external validation, lack of pilot testing, and absence of outcome evaluation, and discuss how these limitations affect the credibility and applicability of the proposed framework. Reviewer #2: Major strengths • Timely, practice‑oriented topic in an LMIC • Focus on AI integration in a large academic medical centre in Pakistan addresses a real gap, as most AI‑change papers are conceptual or from HICs. • Multi‑source qualitative design • Three phases (leadership interviews, multidisciplinary workshop, then action‑plan development) create a coherent, developmental trajectory from perceptions → co‑design → framework. • Inclusion of leaders, faculty, and students increases the breadth of perspectives despite a single‑site limitation. • Explicit change‑management lens (SWITCH) • Clear mapping of findings to “Direct the Rider / Motivate the Elephant / Shape the Path” gives the action plan a structured and theoretically anchored form. Recommended modifications • In Abstract and Methods, standardize to one primary descriptor, e.g.: “We conducted an exploratory qualitative study using reflexive thematic analysis of interviews and a co‑design workshop, followed by collaborative development of an action plan.” At the start of Methods, add a short “Study design and phases” paragraph that clearly lists: Phase I: Individual semi‑structured interviews with institutional leaders and faculty. Phase II: Co‑design workshop with multidisciplinary participants, using SWITCH as a sensitizing framework. Phase III: Collaborative development of a SWITCH‑aligned action plan based on Phases I–II. • In the discussion, there are many repetitions about the SWITCH model's benefits; keep it once at the beginning. • Break the limitations into bullet points or paragraphs • Check the conflict of interest statement and correct • Check for spelling mistakes (pliot, emperical, effectiveness, particpants) ********** what does this mean? ). 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If this link does not appear, there are no attachment files.] To ensure your figures meet our technical requirements, please review our figure guidelines: https://journals.plos.org/plosone/s/figures You may also use PLOS’s free figure tool, NAAS, to help you prepare publication quality figures: https://journals.plos.org/plosone/s/figures#loc-tools-for-figure-preparation . NAAS will assess whether your figures meet our technical requirements by comparing each figure against our figure specifications. PLoS One. 2026 Apr 17;21(4):e0346582. doi: 10.1371/journal.pone.0346582.r006 Author response to Decision Letter 3 Article notes Copyright and License information Collection date 2026. PMC Copyright notice 18 Mar 2026 2026/03/16 To the Editor PLOS One Subject: Response to Reviewers’ Comments Dear Editor, Thank you for sharing the reviewers’ comments and as per their suggestions we have revised our manuscript. All comments are responded below against each point and manuscript has been edited accordingly. We have submitted our revised manuscript in 3 separate files: 1. This document detailing the changes made 2. a copy of the manuscript with track-changes 3. a clean copy of the revised manuscript • Reviewer #1: The manuscript addresses a timely and contextually important issue; however, before it can be considered for publication, several substantive concerns require further attention. In particular,1. The study should explicitly and consistently define its methodological orientation (e.g., exploratory qualitative study using reflexive thematic analysis) and ensure alignment between research questions, data collection methods (interviews, workshop, meetings), and analytic procedures. Response: We thank the reviewer for the opportunity to further clarify our methodological alignment. We have now explicitly labelled the study as an exploratory qualitative study using reflexive thematic analysis in the opening of the Methods section. Now the study objectives align with the research question, methodology and data analysis. • 2. Provide a clearer step-by-step account of how codes were generated, how themes were refined, and how decisions were made during analysis. A visual analytic map or thematic development table would enhance transparency. Response: The methodology section has been revised to explicitly detail the systematic steps of thematic analysis, ensuring greater clarity and transparency in the research process. We have added a Visual Analytic Map (Figure 1) to the Methodology section to enhance transparency. This figure illustrates the iterative process of manual coding and clarifies how the workshop acted as a validation bridge between the initial inductive analysis and the final deductive mapping to the Switch Model. • 3. Move beyond descriptive summaries of what participants said. For each theme, include interpretive commentary that explains why these patterns matter, how they relate to existing literature, and what tensions or contradictions emerged. Response: Agreed, in each theme we have added interpretive commentaries where possible. • 4. Integrate quotations analytically rather than illustratively. Each quote should be followed by interpretation that demonstrates how it advances conceptual understanding rather than merely exemplifying a theme. Response: agree all the quotations are now integrated analytically rather than illustratively. • 5. Given the insider status of several authors, include a more explicit reflexivity statement detailing positionality, power dynamics, and how potential bias was managed throughout data collection and analysis. Response: We acknowledge our insider status within the hospital. To manage potential bias, data collection was framed as collaborative problem solving rather than a top down evaluation, reducing perceived hierarchies. Trustworthiness was further ensured through methodological triangulation, comparing interview data with workshop findings, and guaranteeing anonymity to encourage candid participation and mitigate social desirability bias. We have tried to explain this in the methods section. • 6. Specify how interview findings, workshop discussions, and planning meetings were compared, reconciled, and synthesized into the final action plan. The analytic integration process requires clearer articulation. Response: This has been modifed and rewritten in the methodology. • 7. Provide a more critical comparison with alternative change-management frameworks, explaining precisely what conceptual gap the SWITCH model fills in this context. Response: Thank you for pointing this out. We have now added the critical comparison of models of change now in the introduction section. • 8. Clearly distinguish the action plan from a policy guideline by articulating the theoretical insights derived from the data. Consider presenting a prioritization matrix or conceptual model that demonstrates analytic synthesis rather than listing components. Response: We agree that a distinction between a policy guideline and a research-derived action plan is vital. We have utilized the Switch framework (Rider, Elephant, and Path check table 2) not merely as a categorization tool, but as a conceptual model for analytic synthesis. This framework allows us to demonstrate how the qualitative themes translate into specific behavioural and environmental changes. The action plan is intentionally structured for practical utility to ensure knowledge translation which is a key goal of applied qualitative research. While it may resemble a policy guideline in its clarity, it is distinguished by its inductive origin. To emphasize this, we have added explicit links between the proposed actions and the emergent themes from the data. All edits are in track changes. • 9. Explicitly acknowledge insider bias, absence of external validation, lack of pilot testing, and absence of outcome evaluation, and discuss how these limitations affect the credibility and applicability of the proposed framework. Response: We thank the reviewer for this critical observation. We agree that the use of a specific model and the current stage of the framework's development present significant limitations to its immediate applicability. We have revised the Limitations section to explicitly discuss how the insider bias, lack of pilot testing and how the SWITCH model may have introduced interpretive bias and how the absence of external validation and outcome evaluation necessitates caution when generalizing these findings. • Reviewer #2: Major strengths • Timely, practice oriented topic in an LMIC • Focus on AI integration in a large academic medical centre in Pakistan addresses a real gap,as most AI change papers are conceptual or from HICs. • Multi source qualitative design• Three phases (leadership interviews, multidisciplinary workshop, then action plan development) create a coherent, developmental trajectory from perceptions → co design → framework. • Inclusion of leaders, faculty, and students increases the breadth of perspectives despite a single site limitation.• Explicit change management lens (SWITCH)• Clear mapping of findings to “Direct the Rider / Motivate the Elephant / Shape the Path” gives the action plan a structured and theoretically anchored form. Response: thank you for these positive encouraging comments. • Recommended modifications• In Abstract and Methods, standardize to one primary descriptor, e.g.: “We conducted an exploratory qualitative study using reflexive thematic analysis of interviews and a co design workshop, followed by collaborative development of an action plan.”At the start of Methods, add a short “Study design and phases” paragraph that clearly lists:Phase I: Individual semi structured interviews with institutional leaders and faculty.Phase II: Co design workshop with multidisciplinary participants, using SWITCH as a sensitizing framework.Phase III: Collaborative development of a SWITCH aligned action plan based on Phases I–II. Response: Thank you for this suggestion. We have standardized the primary descriptor of the study design throughout the Abstract and Methods sections to ensure clarity and consistency. As requested, a "Study design and phases" subsection has been added to the beginning of the Methods section. • • In the discussion, there are many repetitions about the SWITCH model's benefits; keep it once at the beginning. Response: Thankyou we have now edited the discussion and benefits of SWITCH have been merged together. • • Break the limitations into bullet points or paragraphs. Response: Agree, we have now written the study limitations as bullet points for clarity. • • Check the conflict of interest statement and correct. Response: Thank you for pointing out this error. The statement has been rewritten. • • Check for spelling mistakes (pliot, emperical, effectiveness, particpants). Response: thank you we have now removed all grammatical errors and spelling mistakes. Attachment Submitted filename: response to reviewers AI paper mar 2026.docx pone.0346582.s004.docx (20.4KB, docx) PLoS One. doi: 10.1371/journal.pone.0346582.r007 Decision Letter 3 Syed Hani Abidi Syed Hani Abidi Academic Editor Find articles by Syed Hani Abidi Author information Copyright and License information Roles Syed Hani Abidi : Academic Editor © 2026 Syed Hani Abidi This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. PMC Copyright notice 22 Mar 2026 Evidence-Based Action Plan for Integrating Artificial Intelligence in an Academic Medical Centre-A Multidisciplinary Approach PONE-D-25-46411R3 Dear Dr. Jafri, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. 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Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact [email protected]. Kind regards, Syed Hani Abidi Academic Editor PLOS One Additional Editor Comments (optional): Reviewers' comments: Reviewer's Responses to Questions Comments to the Author Reviewer #1: All comments have been addressed ********** 2. Is the manuscript technically sound, and do the data support the conclusions??> Reviewer #1: Yes ********** 3. Has the statistical analysis been performed appropriately and rigorously? -->?> Reviewer #1: N/A ********** 4. Have the authors made all data underlying the findings in their manuscript fully available??> The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.--> Reviewer #1: Yes ********** 5. Is the manuscript presented in an intelligible fashion and written in standard English??> Reviewer #1: Yes ********** Reviewer #1: No furthere comments.The paper address all the comments. The manuscript is now technically sound, ethically robust, and provides a valuable contribution to the field of AI integration in healthcare. I find no further concerns regarding dual publication or research ethics. All previous queries have been successfully addressed, and I recommend the paper for immediate acceptance ********** what does this mean? ). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). 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For information about this choice, including consent withdrawal, please see our For information about this choice, including consent withdrawal, please see our For information about this choice, including consent withdrawal, please see our For information about this choice, including consent withdrawal, please see our Privacy Policy ..--> Reviewer #1: No ********** PLoS One. doi: 10.1371/journal.pone.0346582.r008 Acceptance letter Syed Hani Abidi Syed Hani Abidi Academic Editor Find articles by Syed Hani Abidi Author information Copyright and License information Roles Syed Hani Abidi : Academic Editor © 2026 Syed Hani Abidi This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Syed Hani Abidi Academic Editor PLOS One Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials Attachment Submitted filename: October 2025 point by cpoint response.docx pone.0346582.s001.docx (22.5KB, docx) Attachment Submitted filename: PONE-D-25-46411R1.docx pone.0346582.s002.docx (15.7KB, docx) Attachment Submitted filename: Jan 2026 response to plos one reviewers SWITCH.docx pone.0346582.s003.docx (24.8KB, docx) Attachment Submitted filename: response to reviewers AI paper mar 2026.docx pone.0346582.s004.docx (20.4KB, docx) Data Availability Statement All relevant data are within the manuscript. 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