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Learn more: PMC Disclaimer | PMC Copyright Notice Nurs Health Sci . 2026 Apr 13;28(2):e70332. doi: 10.1111/nhs.70332 Search in PMC Search in PubMed View in NLM Catalog Add to search Perceptions of Intensive Care Nurses Toward Artificial Intelligence Technologies: A Qualitative Study Dilek Yildirim Dilek Yildirim 1 Faculty of Health Sciences, Department of Nursing, İstanbul Aydin University, İstanbul, Turkey Find articles by Dilek Yildirim 1, ✉ , Cennet Çiriş Yildiz Cennet Çiriş Yildiz 1 Faculty of Health Sciences, Department of Nursing, İstanbul Aydin University, İstanbul, Turkey Find articles by Cennet Çiriş Yildiz 1 , Emine Ergin Emine Ergin 2 Hamidiye Faculty of Health Sciences, Department of Midwifery, University of Health Sciences, İstanbul, Turkey Find articles by Emine Ergin 2 Author information Article notes Copyright and License information 1 Faculty of Health Sciences, Department of Nursing, İstanbul Aydin University, İstanbul, Turkey 2 Hamidiye Faculty of Health Sciences, Department of Midwifery, University of Health Sciences, İstanbul, Turkey * Correspondence: Dilek Yildirim ( [email protected] ; [email protected] ) ✉ Corresponding author. Revised 2026 Mar 23; Received 2025 Dec 5; Accepted 2026 Mar 30; Issue date 2026 Jun. © 2026 The Author(s). Nursing & Health Sciences published by John Wiley & Sons Australia, Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. PMC Copyright notice PMCID: PMC13071536 PMID: 41968950 ABSTRACT Intensive care nurses play a pivotal role in patient care; however, their perceptions and concerns regarding artificial intelligence (AI) in intensive care units remain limited. This study aimed to explore ICU nurses' views on AI to support effective integration strategies. A qualitative descriptive design with interpretive elements was conducted between September and December 2025 in multiple hospitals in Istanbul, Turkey. Using purposive snowball sampling, 22 ICU nurses participated in semi‐structured, face‐to‐face interviews. Data were analyzed inductively using interpretive description. Six themes were identified: knowledge and awareness of AI, experiences with AI, impact on nursing care, role in clinical decision‐making, ethical and safety risks, and educational needs and future expectations. Findings revealed that nurses had limited and fragmented knowledge of AI, with mostly indirect exposure. While AI was perceived as having the potential to improve care quality and support clinical decisions, concerns were raised regarding loss of autonomy, data security, device errors, and accountability. Participants also emphasized the irreplaceable role of human presence in care and highlighted a clear need for structured education and institutional support. Keywords: artificial intelligence, clinical decision‐making, healthcare innovation, intensive care unit, nursing, technology integration Summary ICU nurses have limited and heterogeneous knowledge of AI, indicating a need for structured and comprehensive training programs. Nurses view AI as a supportive tool that can enhance clinical decision‐making, reduce errors, and improve patient safety but cannot replace humanistic aspects of care. Ethical concerns including data security, system errors, and unclear accountability must be addressed through institutional policies and legal frameworks. Excessive reliance on AI may threaten professional autonomy; therefore, AI should be implemented with clearly defined boundaries that preserve the nurse's role. Effective integration of AI into ICU practice requires organizational readiness, technological infrastructure, and continuous competency development. 1. Introduction Intensive care nurses are central to maintaining and improving patient health in complex, high‐stakes clinical environments. The introduction of artificial intelligence (AI) technologies in healthcare has the potential to transform multiple aspects of nursing practice, from patient monitoring and predictive analytics to clinical decision support. These innovations can enhance the quality and safety of care, improve efficiency, and help ICU nurses manage increasing workloads in intensive care units (ICUs) (Ahmed and Elderiny 2024 ; Mohamed Abd El‐Monem et al. 2023 ). In high‐acuity environments such as ICUs, nurses manage large volumes of complex, time‐sensitive patient data and make high‐stakes decisions under pressure. These settings are characterized by rapid changes in patient condition, continuous monitoring, and advanced technological interventions (Bi et al. 2025 ; Elhaddad and Hamam 2024 ; Vernic et al. 2025 ) AI technologies can provide supportive tools in this context, such as continuous monitoring of vital signs, detection of subtle trends or deteriorations, and clinical decision support to enhance nurses' judgment and response (Bi et al. 2025 ; Porcellato et al. 2025 ). Given the unique demands and technology‐intensive nature of ICU care, studying ICU nurses' perceptions offers critical insights into the opportunities and challenges of AI integration, which may not be as apparent in other clinical units. 2. Background AI is a broad and evolving concept that encompasses a wide range of computational techniques developed over several decades. In the context of this study, AI is operationally defined as data‐driven computational systems capable of performing tasks that typically require human intelligence, such as pattern recognition, clinical decision support, and predictive analytics (Esteva et al. 2017 ; Jiang et al. 2017 ; Topol 2019 ). More specifically, the term refers to contemporary AI approaches based on machine learning, including deep learning models, large language models, and AI‐enabled clinical decision support systems that are increasingly utilized in intensive care settings (Almagharbeh 2025 ; Jiang et al. 2017 ; Topol 2019 ). AI has been present in medicine in various forms since the mid‐20th century; however, recent advances in machine learning have significantly enhanced its capabilities, applicability, and visibility in clinical practice (Jiang et al. 2017 ; Topol 2019 ). Despite the promising benefits, the integration of AI into clinical practice presents a set of challenges and concerns for ICU nurses. Evidence indicates that ICU nurses may experience apprehension about AI's impact on professional identity, the nurse–patient relationship, and ethical standards of care (Alsenany et al. 2024 ; Sabra et al. 2023 ). Specific concerns include the potential for workforce reduction, the depersonalization of care, and dilemmas associated with data privacy and patient autonomy (Alatawi 2022 ; Sabra et al. 2023 ). These issues underscore the importance of addressing ICU nurses' perceptions and experiences to ensure that AI implementation supports, rather than undermines, the core values of nursing practice. A key barrier to the effective adoption of AI is limited knowledge and experience among ICU nurses. Several studies report that a significant proportion of ICU nurses lack sufficient understanding of AI concepts, functionalities, and practical applications, which may impede adoption and optimal utilization (Ahmed and Elderiny 2024 ; El‐Sayed et al. 2025 ; Erkayıran and Aslan 2025 ). Well‐designed education and training programs are essential to equip nurses with the skills and confidence required to engage effectively with AI systems, navigate ethical considerations, and optimize patient outcomes (Amin et al. 2025 ; Atalla et al. 2024 ; Mohamed Abd El‐Monem et al. 2023 ). ICU Nurses' perceptions of AI are not solely shaped by technical knowledge but also by their professional, ethical, and emotional concerns. While AI can support clinical decision‐making, reduce workload, and improve care efficiency, nurses often worry that technological interventions may compromise the humanistic dimension of care, an essential aspect of nursing identity (Gumus and Alan 2025 ; Tuncer and Tuncer 2024 ). Addressing these concerns requires integrated strategies that combine technical training with attention to ethical, social, and professional implications of AI deployment. Understanding how ICU nurses perceive AI is therefore critical for successful implementation. Insight into factors that facilitate or hinder acceptance of AI technologies can guide nurse leaders, educators, and healthcare organizations in designing effective training, policies, and implementation strategies. This approach ensures that AI integration enhances patient care, preserves professional standards, and supports ICU nurses' job satisfaction (Ecarnot et al. 2022 ; Elsayed and Sleem 2021 ). In addition, evidence suggests that nurses' attitudes toward new technologies, including AI, are shaped not only by individual competencies but also by institutional readiness and perceived impact on care practices, which play a decisive role in technology adoption (Vasquez et al. 2023 ). In this context, the present study aims to explore ICU nurses' perceptions of AI technologies in depth, with the goal of understanding the current state of AI tool usage in clinical settings and providing insights into how future applications may evolve. Investigating how nurses perceive AI and its impact on their professional practice represents an important step toward enhancing the quality of healthcare services. 3. Materials and Methods 3.1. Study Design This research was designed as a qualitative study aiming to gain an in‐depth understanding of intensive care nurses' perceptions, expectations, and concerns regarding the integration of AI technologies into critical care practice. Data were collected from intensive care nurses working in various hospitals in Istanbul, Turkey, between September 2025 and December 2025 using a snowball sampling strategy. The Standards for Reporting Qualitative Research (SRQR) guidelines were adopted to ensure methodological rigor, credibility, and transparency throughout the research process. The SRQR guidelines were followed to enhance transparency, rigor, and completeness in reporting. These guidelines informed key aspects of the study, including a clear description of the research design, sampling strategy, data collection procedures, data analysis process, researcher reflexivity, and strategies used to ensure trustworthiness (e.g., triangulation and audit trail) (O'Brien et al. 2014 ; Thorne 2016 ). 3.2. Researcher Reflexivity The research team consisted of three nurse researchers with experience in intensive care, nursing informatics, and qualitative research methods. Two researchers had prior experience with AI‐assisted clinical tools, which informed the interview guide and data interpretation. To minimize potential bias, reflexive journals were maintained, and regular team discussions were conducted to reflect on the researchers' perspectives and assumptions. 3.3. Sample A qualitative descriptive design enriched with interpretive elements was utilized to capture the complexity and contextual depth of nurses' experiences with emerging technologies in different intensive care settings, including AI‐based decision‐support systems, smart alarms, and patient‐monitoring devices. In addition to purposive sampling, a snowball sampling strategy was used to reach additional participants through peer referrals. The snowball sampling approach enabled the recruitment of information‐rich participants with diverse professional backgrounds by allowing initially contacted nurses to refer additional colleagues who met the inclusion criteria (Noy 2008 ). To minimize potential sampling bias associated with snowball recruitment, efforts were made to ensure diversity in participants' demographic and professional characteristics (e.g., years of experience, ICU type, and institutional background). Initial participants were selected from different units, and referrals were monitored to avoid over‐representation of closely connected individuals. A purposive sampling strategy was employed to recruit nurses who were directly involved in patient care and who were likely to provide rich, experience‐based insights into the use of AI tools in ICUs. In this study, a minimum of 1 year of ICU experience was considered sufficient to ensure that participants had developed familiarity with the clinical environment, interdisciplinary workflows, and routine use of digital health technologies. Rather than focusing solely on duration of experience, this criterion aligns with qualitative research principles emphasizing the inclusion of information‐rich participants who are actively engaged in clinical practice and capable of reflecting on their experiences. The inclusion criteria consisted of the following: being currently employed as an intensive care nurse, having at least 1 year of work experience in intensive care, willingness to participate. Nurses who were on leave at the time of data collection or refused to participate were excluded from the study. Potential participants were identified through unit nurse managers, who provided the research team with contact information for eligible nurses. Interested nurses were subsequently informed‐verbally and in writing‐about the study's purpose, expected contribution, interview process, and confidentiality measures. Recruitment and interviews continued until data saturation was achieved, defined as the point at which no new themes, categories, or conceptual insights emerged from the data (Guest et al. 2006 ; Saunders et al. 2018 ). Saturation was assessed through ongoing concurrent data analysis, whereby successive interviews were reviewed to determine whether additional data contributed novel information or merely reinforced existing patterns. Importantly, nurses did not receive formal training related to AI technologies specifically for the study; however, one participant reported prior exposure to an AI‐related training session. Most perspectives were therefore based on prior clinical experience, institutional meetings, personal interactions with digital health systems, and general understanding of AI‐assisted tools within the hospital. Recruitment and interviews continued until data saturation was achieved, defined as the point at which no new concepts or variations emerged, following COREQ recommendation. The final sample consisted of 22 intensive care nurses. 3.4. Measure Development The semi‐structured interview guide was developed through a structured review of the literature on AI in healthcare, nursing informatics, digital transformation in intensive care settings, and technology acceptance frameworks. A comprehensive literature review on AI in healthcare, nursing informatics, digital transformation in ICUs, and technology acceptance models guided the development of the preliminary interview questions (Table 1 ). This process aimed to identify key conceptual domains relevant to nurses' interactions with and perceptions of AI technologies. The initial pool of questions was informed both by existing literature and by the theoretical underpinnings of technology adoption and clinical decision‐support systems, rather than directly adopting items verbatim from prior studies. TABLE 1. Interview guide questions. Number Interview guide questions 1 What is your knowledge regarding the use of artificial intelligence (AI) technologies in the intensive care setting? 2 Are AI‐enabled systems or devices currently employed in your intensive care unit? If so, please describe your experience with them. 3 In your opinion, how do AI technologies impact the delivery of nursing care? 4 How do you perceive the role of AI systems in supporting clinical decision‐making processes? 5 In your opinion, is it possible for AI to replace nurses? Please explain your reasoning. 6 How does working with AI technologies make you feel? 7 In your opinion, does AI pose more of an advantage or a potential risk regarding patient safety? 8 In your opinion, do these technologies present any ethical or professional risks? 9 In your opinion, what kind of education or training should nurses undergo to effectively work with AI technologies? 10 What is your perspective on the potential future impact of AI on intensive care nursing practice? Open in a new tab To enhance content validity and clarity, the preliminary interview guide was pilot‐tested with three intensive care nurses who met the inclusion criteria but were not included in the final sample. These pilot interviews were used to assess question comprehensibility, relevance, and flow, as well as to identify potential ambiguities or leading expressions. Minor revisions were made to improve wording, sequencing, and coherence of the questions. The final interview guide was organized around the following domains: prior experiences with AI‐based tools, perceived impact on clinical decision‐making, concerns related to autonomy and professional roles, ethical considerations and accountability, and expectations regarding the future integration of AI into intensive care practice. Reflexivity was considered throughout the development process. The researchers, all of whom have professional backgrounds in nursing and experience in intensive care settings, acknowledged that their prior knowledge and attitudes toward AI could influence both the framing of questions and interpretation of responses. To mitigate potential bias, the interview questions were designed to remain open‐ended and non‐directive, allowing participants to freely express their perspectives. 3.5. Data Collection Informed consent, both written and verbal, was obtained from all participants prior to data collection. Recruitment and data collection proceeded concurrently until data saturation was achieved, defined as the point at which no new themes or meaningful variations emerged from the interviews. Data were collected through semi‐structured, face‐to‐face interviews conducted in a private and quiet room within the hospital environment to ensure confidentiality and minimize interruptions. Interviews were conducted by members of the research team who were trained in qualitative interviewing techniques. Although the interviewers had prior clinical experience in ICUs, care was taken to maintain a professional distance and to minimize the influence of pre‐existing assumptions on the interview process. Each interview lasted approximately 30–50 min. With participants' permission, all interviews were audio‐recorded using a digital recording device. In addition to audio recordings, field notes were taken during and immediately after the interviews to capture contextual and non‐verbal information, including tone of voice, pauses, emotional expressions, and body language. These observations were incorporated into the analytical process to enrich the interpretive depth of the findings. 3.6. Data Analysis All interviews were transcribed verbatim following the removal of personal identifiers to ensure confidentiality. The transcripts were independently read multiple times by three researchers to achieve immersion in the data and gain a comprehensive understanding of participants' experiences. Data analysis was conducted using the interpretive description methodological framework, which is particularly suitable for generating clinically meaningful insights grounded in participants' real‐world experiences. An inductive analytic approach was adopted. Initially, line‐by‐line open coding was performed to identify meaningful units within the data. These initial codes were then systematically compared, grouped, and organized into preliminary categories. Through an iterative process of constant comparison, subthemes were developed and subsequently clustered into broader themes that reflected shared patterns and conceptual relationships across participants. Data collection and analysis were conducted concurrently, enabling the research team to monitor emerging patterns and determine the point of data saturation, defined as the stage at which no new themes or variations were identified. To enhance analytic rigor, regular meetings were held among the three researchers to compare coding decisions, discuss discrepancies, and refine emerging themes. Coding consistency was strengthened through intercoder agreement, with differences resolved through discussion and consensus, supported by revisiting the original transcripts. Reflexivity was maintained throughout the research process, with researchers critically reflecting on their own professional backgrounds, assumptions, and potential influences on data interpretation. Analytic memos were recorded to document these reflections and to ensure that interpretations remained grounded in participants' accounts. Several strategies were employed to ensure trustworthiness, including investigator triangulation, peer debriefing, and maintaining an audit trail documenting all stages of the analytic process. Although formal member checking was not conducted, findings were continuously reviewed within the research team to enhance credibility and confirmability. Representative quotations were selected to illustrate the themes, enhance transparency, and preserve the authenticity of participants' voices. The final thematic structure was reviewed and agreed upon by all members of the research team. 3.7. Ethical Considerations Before data collection, the research team provided participants with detailed information about the study's purpose, procedures, expected duration, potential risks and benefits, voluntary nature, and confidentiality measures. Ethical approval for the study was obtained from the Istanbul Aydın University Ethics Committee (2025/198). Written informed consent was obtained prior to initiating each interview. Participants were assured that their decision to participate or withdraw at any time would not affect their employment status or professional relationships. All data were anonymized and stored securely. The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki, emphasizing respect for autonomy, privacy, and protection of human subjects. 4. Results The demographic and professional characteristics of the nurses participating in the study are presented in Table 2 . Overall, the sample consisted predominantly of young nurses in their late twenties (mean age: 28.09 ± 3.85 years) with relatively early‐career experience, including an average of 4.68 ± 2.77 years in the profession and 2.82 ± 2.15 years in intensive care settings. The mean length of experience in the current ICU was 2.09 ± 1.38 years. TABLE 2. Sociodemographic and profession characteristics of critical care nurses ( n = 22). Characteristics (Mean ± SD; range) Age (years) 28.090 ± 3.853; 22–36 Professional experience (years) 4.681 ± 2.766; 2–10 ICU experience (years) 2.818 ± 2.152; 1–9 Current ICU experience (years) 2.090 ± 1.377; 1–5 n (%) Gender Female 19 (86.4) Male 3 (13.6) Educational status Bachelor's Degree 14 (13.6) Master's Degree 8 (86.4) Type of intensive care unit Medical 11 (50.0) Surgical 3 (13.6) Coronary 8 (36.4) Hospital of employment University Hospital 8 (36.4) Training and Research Hospital 3 (13.6) Public Hospital 5 (22.7) Private Hospital 6 (27.3) Working hours Working in Shifts 22 (100.0) Receiving training on artificial intelligence applications Yes 1 (4.5) No 21 (95.5) Encountering artificial intelligence applications Yes 21 (95.5) No 1 (4.5) Open in a new tab The majority of participants were female (86.4%) and highly educated, with all nurses holding at least a bachelor's degree and a substantial proportion (86.4%) having completed a master's degree. Participants were drawn from diverse ICU settings, including medical (50.0%), surgical (13.6%), and coronary ICUs (36.4%), and worked across different hospital types such as university, training and research, state, and private hospitals. All participants worked in a shift system. Despite being a relatively young and well‐educated group, only one participant (4.5%) had received formal training on AI, although the vast majority (95.5%) reported some level of exposure to AI applications. This finding is noteworthy given the limited depth of AI‐related knowledge identified in the sample. In this study, intensive care nurses' perceptions of AI were analyzed under six main themes and their corresponding subthemes. Participants' views were interpreted while maintaining content integrity, and the themes were supported with direct participant statements (Table 3 ). TABLE 3. Themes. Ana Tema Alt Temalar Theme 1: Knowledge and Awareness Level Regarding Artificial Intelligence 1.1. Lack of Knowledge 1.2. Partial Technological Awareness Theme 2: Experiences and Practices of Using AI 2.1. Indirect and Informal Use of AI 2.2. Institutional and Educational Limitations Theme 3: Impact of AI on Nursing Care 3.1. Potential to Improve Care Quality 3.2. Inability to Replace Human‐Centered Care Theme 4: Role of AI in Clinical Decision‐Making 4.1. Decision‐Support Role 4.2. Necessity of Human Oversight 4.3. Concern About Loss of Professional Role Theme 5: Ethical, Safety, and Professional Responsibility Risks 5.1. Concerns About Data Security 5.2. Risk of System and Device Errors 5.3. Uncertainty of Ethical Responsibility Theme 6: Educational Needs and Future Expectations 6.1. Lack of Education and Competence 6.2. Future‐Oriented Expectations Open in a new tab 4.1. Theme 1. Level of Knowledge and Awareness About Artificial Intelligence 4.1.1. Subtheme 1.1. Knowledge Deficit Some nurses reported having little to no knowledge about the concept of AI. These participants were unable to provide a concrete definition of AI or its use in the clinical setting, perceiving the technology as a rather ambiguous concept. Statements such as “I have no knowledge at all” (Nurse 1) and “Honestly, I don't really have an idea” (Nurse 4) clearly reflect this situation. Some nurses also acknowledged their lack of understanding by saying, highlighting a group with a very low level of awareness regarding AI integration. 4.1.2. Subtheme 1.2. Partial Technological Awareness Although some nurses did not fully grasp AI, they associated certain features of intensive care devices with AI‐like functions. These participants described the capabilities of existing technologies—such as automation, alarm management, and trend analysis—as resembling aspects of AI. Some participants, however, perceived routine functions of monitors, ventilators, or alarm systems as AI, highlighting the conceptual ambiguities surrounding AI in clinical practice. For instance, one nurse stated, “We set upper and lower limits on the monitors, and the device gives an alert” (Nurse 2), drawing attention to the system's automatic decision‐support function. Another participant noted that smart alarm features provide alerts by analyzing trends (Nurse 11). These statements indicate that nurses generally perceive AI as an advanced extension of the technologies they already use. No significant differences in AI knowledge were observed across ICU types, participant age, or gender, though the majority of the sample consisted of young female nurses (mean age 28 years; 86% female). 4.2. Theme 2. Experiences and Practices of Using AI 4.2.1. Subtheme 2.1. Indirect and Informal Use of AI Although there were no systems in place that directly employed AI, nurses reported that devices such as monitors and ventilators exhibited AI‐like functionalities through their alarm systems. For example, “The ventilator and monitors give instant alerts when limits are exceeded” (Nurse 3) indicates that these technologies are perceived as automatic decision‐support tools in clinical practice. Some nurses also associated the ability of monitors to track multiple patients simultaneously with AI (Nurse 2). Despite institutional limitations, some nurses reported individual use of AI tools. Statements such as “I use AI applications for dose calculations” (Nurse 2) and “We use ChatGPT” (Nurse 5) suggest that AI is used informally at the individual level. 4.2.2. Subtheme 2.2. Institutional and Educational Limitations The majority of nurses reported that their institutions did not have AI‐based systems. Statements such as “Our hospital does not use an AI‐supported system” (Nurse 6) and “The use of AI in state hospitals is very limited” (Nurse 1) indicate that healthcare institutions are not yet technologically prepared for AI integration. “I currently work in a unit without any AI‐supported system. We mostly perform manual monitoring. I believe the hospital infrastructure is not ready for this. Therefore, technical and administrative preparations are needed for AI to enter the clinic” (Nurse 15), “Nurse 16 reported that their hospital provides minimal support for AI systems, limiting opportunities for practical use.” further illustrates these infrastructural limitations. In parallel, nurses emphasized gaps in education and competence. Statements such as “Technology training is essential” (Nurse 10) and “I believe we must receive training to work with AI…” (Nurse 22) highlight the need for structured education to support effective AI use. Despite institutional limitations, some nurses utilize AI tools on an individual level. For instance, “I use AI applications for dose calculations” (Nurse 2) and “We use ChatGPT” (Nurse 5) indicate that personal use of AI can indirectly contribute to professional practice. Educational gaps were also emphasized. Only one nurse had received formal AI training, which was reported as helpful but insufficient, while another participant had no prior AI experience, highlighting the variability in preparedness among staff. Participants stressed the need for structured education and technological competence to enable safe and effective AI use (Nurse 10; Nurse 22). No clear differences were observed between hospital types (university, state, training/research, private) or ICU settings. Coronary ICU nurses more frequently emphasized alarm management, although this pattern was not statistically evaluated. 4.3. Theme 3. Impact of Artificial Intelligence on Nursing Care 4.3.1. Subtheme 3.1. Potential to Enhance Care Quality Nurses believe that AI can accelerate decision‐making processes and reduce the margin of error. One nurse stated, “We reach a diagnosis earlier, which makes our work easier” (Nurse 2), highlighting the perception of AI as a time‐saving tool in care delivery. Similarly, “It increases patient safety” (Nurse 11) reflects the view that the technology's ability to anticipate risks is considered a positive contribution to care quality. 4.3.2. Subtheme 3.2. Inability to Replace Human Care Almost all nurses emphasized that AI cannot fulfill the humanistic aspects of nursing care. Statements such as “It cannot provide direct, hands‐on care” (Nurse 2) and “Physical examination and touch are necessary” (Nurse 3) underscore the importance of human interaction in care. Additionally, elements such as compassion, empathy, and intuitive decision‐making are regarded as beyond the capabilities of AI, as one nurse explicitly stated, “Devices cannot provide compassion and empathy” (Nurse 7); Nurse 14 stated that AI alerts are helpful but cannot replace her clinical judgment. 4.4. Theme 4. The Role of AI in Clinical Decision‐Making 4.4.1. Subtheme 4.1. Decision‐Support Function Nurses indicated that AI could assist in clinical decision‐making, particularly by providing advantages in early warning systems and data analysis. Statements such as “We can make faster and more accurate decisions” (Nurse 3) and “Early warnings make decision‐making easier” (Nurse 11) support this perspective. 4.4.2. Subtheme 4.2. Necessity of Human Oversight Conversely, nurses strongly emphasized that AI should not be the sole determinant in decision‐making processes. “The final decision should be made by the healthcare professional” (Nurse 4) and “AI can be supportive, but I believe the final decision must always lie with the healthcare professional. Patient conditions sometimes rely not only on data but also on intuition and clinical experience. AI cannot interpret every situation” (Nurse 19) highlight the importance of human oversight for professional autonomy and clinical responsibility. 4.4.3. Subtheme 4.3. Concern Over Loss of Professional Role Some nurses expressed concern that excessive reliance on AI could lead to a loss of professional roles. This is evident in the statement, “If AI does everything, what will my role be?” (Nurse 8). Another nurse noted, “If AI is overly authorized, the nurse's professional role may weaken. This could pose a risk of diminishing the value of nurses in the future. The humanistic aspects and expertise of our profession must be preserved” (Nurse 20), indicating apprehension about potential role displacement. 4.5. Theme 5. Risks Related to Ethics, Safety, and Professional Responsibility 4.5.1. Subtheme 5.1. Data Security Concerns Nurses identified data security as one of the primary risks associated with AI systems. Statements such as “It is unclear who has access to the data” (Nurse 9) and “Data could be transmitted to others” (Nurse 3) reflect concerns about confidentiality and patient privacy. These gaps suggest that hospitals need clear policies and secure infrastructure before AI can be safely integrated. 4.5.2. Subtheme 5.2. Risk of System and Device Errors Some nurses expressed concerns that errors in AI‐supported devices could compromise patient safety. For instance, “If the device malfunctions, patient safety is at risk” (Nurse 10) underscores this concern. 4.5.3. Subtheme 5.3. Uncertainty in Ethical Responsibility The issue of who bears responsibility in cases of ethical violations by AI emerged as a professional concern. The question, “If AI commits an ethical violation, who is responsible?” (Nurse 12) highlights the lack of clarity in current systems regarding legal and ethical accountability. The findings suggest a need for regulatory guidelines and professional frameworks to clearly define legal and ethical responsibilities. 4.6. Theme 6. Educational Needs and Future Expectations 4.6.1. Subtheme 6.1. Gaps in Education and Competence Nurses emphasized that effective use of AI is dependent on appropriate training. Statements such as “It is necessary to use electronic devices well” (Nurse 3) and “Technology training is essential” (Nurse 10), as well as “I believe we must receive training to work with AI. We need to know how to operate the device and when to rely on AI versus prioritizing our own clinical judgment” (Nurse 22), highlight the importance of technological competence. Additionally, the need for education on data management is reflected in the statement, “I need to learn how to input data correctly” (Nurse 6). Only one nurse had received formal AI training (helpful but limited), while another participant had no prior AI experience. These outliers underscore variability in preparedness among ICU nurses. 4.6.2. Subtheme 6.2. Future Expectations Nurses believe that AI will play a significant role in intensive care in the future. For example, “It speeds up and organizes care” (Nurse 2) reflects this expectation, while another nurse noted that AI could take over paperwork, saving nurses' time (Nurse 7). However, some nurses also anticipate a slow pace of technology integration: “Technology arrives late; it may not integrate quickly” (Nurse 9). 5. Discussion This study examined nurses' knowledge of AI, their patterns of use, their perceptions of its impact on nursing care, and their views on ethical and safety‐related risks in a comprehensive manner. The findings of this study indicate that a substantial proportion of intensive care nurses have not received formal education on AI applications, which appears to be a significant barrier to its effective integration into clinical practice. This result aligns with previous studies reporting limited awareness and insufficient training among nurses (Alruwaili et al. 2024 ; Tuncer and Tuncer 2024 ). Similarly, Almagharbeh ( 2025 ) reported that more than half of nurses perceived their training as inadequate (Almagharbeh 2025 ). However, beyond confirming existing literature, our findings highlight a clear need for structured and practice‐oriented AI education tailored to nurses' clinical roles (Almagharbeh 2025 ; Nashwan et al. 2025 ). Another important finding is that nurses frequently associated routine technological devices, such as monitors and ventilators, with AI. This suggests that the distinction between automation and AI is not clearly understood in clinical settings. Similar conceptual confusion has been reported in previous studies (Amechghal et al. 2025 ; Lora and Fo 2025 ). This ambiguity may be related to insufficient levels of digital health literacy, as emphasized by Coşkun et al. ( 2024 ). Therefore, improving digital health literacy and providing clearer conceptual frameworks should be prioritized in nursing education. A noteworthy finding of this study is that younger ICU nurses, who might be assumed to be more technologically adept, did not demonstrate higher levels of awareness or knowledge regarding AI applications. This finding challenges the common assumption that younger and well‐educated healthcare professionals are more likely to adopt emerging technologies. Instead, it suggests that familiarity with general technology does not necessarily translate into competence or awareness of AI in clinical practice. This result highlights a critical gap and underscores the need for structured and targeted AI education across all age groups, rather than assuming younger nurses will naturally adapt to such innovations. The perception of AI as beneficial, particularly in clinical decision support, reflects a generally positive attitude among intensive care nurses toward its integration into care processes. Participants emphasized AI's potential to enhance diagnostic speed and reduce errors, which is consistent with studies demonstrating its contribution to clinical effectiveness (Cooper et al. 2025 ; Nashwan et al. 2025 ). Despite this, nurses clearly stated that AI cannot replace the human aspects of care. This finding supports the view that AI should be integrated as a complementary tool rather than a substitute for holistic nursing care and the therapeutic nurse–patient relationship (Coşkun et al. 2024 ). Ethical and safety concerns emerged as one of the most prominent themes in this study. Nurses expressed uncertainty regarding data security, system reliability, and accountability, which contributed to a cautious approach toward AI use. These findings are in line with previous research identifying the lack of clear ethical and legal frameworks as a major barrier to AI implementation (Almagharbeh 2025 ). In particular, uncertainty regarding responsibility in cases of AI‐related errors remains a critical issue in the literature (Ganvir et al. 2025 ; Jeyaraman et al. 2023 ), reinforcing the necessity of maintaining human oversight in clinical decision‐making. Concerns about the potential impact of AI on professional roles were also noteworthy. Nurses working in high‐acuity environments such as ICUs expressed that overreliance on AI could weaken professional autonomy, a finding consistent with previous studies (Ashwini and Padhy 2023 ; Bodur et al. 2025 ). Similarly, qualitative research involving health sciences students revealed concerns about the accuracy of AI‐generated information. Students reported that AI outputs may contain errors, particularly in complex topics, due to the lack of human oversight. This raises important questions regarding information reliability. As a result, students expressed the need to verify AI‐generated information using additional sources. The risk of misleading or incorrect outputs may lead to adverse outcomes, especially in critical care settings (Fawaz et al. 2025 ). As also emphasized by Nashwan et al. ( 2025 ), these findings highlight the importance of maintaining professional roles and keeping clinical judgment central during AI integration. Nevertheless, intensive care nurses tended to position AI primarily as a decision‐support tool, suggesting that they view it as complementary rather than substitutive (Nashwan et al. 2025 ). This perspective indicates that, when used within appropriate boundaries, AI has the potential to enhance care quality and support clinical workflows. Consistently, a study conducted with surgical nurses reported that 78.0% believed AI would facilitate nursing practice (Kahraman et al. 2025 ). Overall, both the study findings and the existing literature indicate a clear need to improve nurses' knowledge and competencies related to AI. Strengthening these competencies is essential to ensure that AI can effectively contribute to healthcare delivery. The findings indicate that nurses experience difficulties in distinguishing conceptual aspects of AI, while at the same time being able to articulate its clinical contributions more concretely. In particular, advantages such as rapid decision‐making, early risk detection, and reduction of error rates were prominently highlighted. However, nurses strongly emphasized that core elements of care such as empathy, human interaction, clinical intuition, and continuous observation cannot be replaced by AI. Additionally, concerns related to data security, patient safety, and accountability appeared to be more pronounced in intensive care settings, which may be attributed to the high‐risk nature of this environment. These findings are consistent with Almagharbeh et al. who emphasize the need for more comprehensive regulations and guidelines for AI use in high‐risk clinical contexts (Almagharbeh 2025 ). 6. Study Limitations This study has several limitations. Although the sample size is typical for qualitative research, the use of snowball sampling may have introduced selection bias, as participants may have referred colleagues with similar perspectives or experiences. In addition, data were collected through self‐report, which may not fully reflect nurses' actual knowledge and experiences. Despite these limitations, the study offers several strengths. Examining AI in nursing practice across multiple dimensions, including knowledge, awareness, practical use, risk perception, and professional impact, provides a meaningful contribution to the literature. Furthermore, the inclusion of nurses' direct statements offers original and timely insights into current clinical practices and perceptions. 7. Conclusions This study demonstrates that ICU nurses' knowledge of AI is limited, their awareness is heterogeneous, and their perceptions of AI are shaped by both potential opportunities and associated risks. Nurses believe that AI can support clinical decision‐making, reduce error rates, and enhance patient safety. However, they emphasize that fundamental elements of nursing care—such as human touch, empathy, physical interaction, and ethical responsibility—cannot be fulfilled by AI. Data security, device malfunctions, and uncertainties regarding ethical accountability are key risk areas that must be considered when integrating AI into clinical practice. In this context, it is essential to develop comprehensive training programs to enhance nurses' knowledge and competencies regarding AI, clarify ethical, legal, and technical standards, and ensure that AI is used in alignment with a human‐centered approach to care. Positioning AI as a tool that supports clinical decision‐making rather than replacing the nursing profession is crucial for both patient safety and the preservation of professional autonomy. 8. Relevance for Clinical Practice The findings of this study offer several important implications for clinical practice in intensive care settings. First, the limited knowledge and conceptual ambiguity nurses hold regarding AI underscore the need for structured, evidence‐based training programs that address both technical competencies and ethical considerations. Enhancing AI literacy among ICU nurses may improve the safe and confident integration of AI‐supported tools into daily workflows. Second, nurses' emphasis on the irreplaceable value of humanistic care such as empathy, touch, and intuitive decision‐making highlights the importance of adopting AI as a supportive rather than substitutive component of care. Clinical leaders should ensure that AI systems are implemented in ways that protect professional autonomy and reinforce, rather than diminish, the central role of the nurse in patient care. Third, concerns about data security, device malfunction, and unclear professional responsibility indicate a pressing need to establish clear institutional guidelines, safety protocols, and legal frameworks before widespread clinical adoption. Policies that clarify accountability in AI‐assisted decision‐making can reduce uncertainty and support ethical practice. Finally, the study suggests that when used appropriately, AI has the potential to strengthen clinical decision‐making, increase patient safety, reduce workload, and streamline documentation processes. Integrating AI tools with well‐defined boundaries and adequate user training may lead to more efficient care processes and improved outcomes in ICUs. Author Contributions Dilek Yildirim: conceptualization, investigation, funding acquisition, writing – original draft, writing – review and editing, visualization, validation, methodology, software, formal analysis, project administration, data curation, supervision, resources. Cennet Çiriş Yildiz: data curation, resources, supervision, project administration, software, formal analysis, visualization, validation, methodology, writing – review and editing, writing – original draft, funding acquisition. Emine Ergin: conceptualization, formal analysis, methodology, supervision, writing – review and editing. Funding The authors have nothing to report. Disclosure No previously published material was reproduced in this study. Ethics Statement Ethical approval for this study was obtained from the Istanbul Aydın University Ethics Committee (2025/198). Consent Written and verbal informed consent was obtained from all participants prior to interviews. Conflicts of Interest The authors declare no conflicts of interest. Acknowledgments The authors thank all participating intensive care nurses for their time and valuable insights. Data Availability Statement De‐identified interview transcripts and study materials are available from the corresponding author upon reasonable request. References Ahmed, S. , and Elderiny s.. 2024. “Intensive Care Nurses' Knowledge and Perception Regarding Artificial Intelligence Applications.” Trends in Nursing and Health Care Journal 8, no. 1: 195–220. 10.21608/tnhcj.2024.250044.1038. [ DOI ] [ Google Scholar ] Alatawi, A.
2022. “Effect of Artificial Intelligence on the Modernization of Nursing Practice.” International Journal of Health Sciences 6: 11506–11517. 10.53730/ijhs.v6nS4.11169. [ DOI ] [ Google Scholar ] Almagharbeh, W. T.
2025. “The Impact of AI‐Based Decision Support Systems on Nursing Workflows in Critical Care Units.” International Nursing Review 72, no. 2: e13011. 10.1111/inr.13011. [ DOI ] [ PubMed ] [ Google Scholar ] Alruwaili, M. M. , Abuadas F. H., Alsadi M., et al. 2024. “Exploring Nurses' Awareness and Attitudes Toward Artificial Intelligence: Implications for Nursing Practice.” DIGITAL HEALTH 10: 1–10. 10.1177/20552076241271803. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Alsenany, S. A. , AlMaraira O. A., and Mansour S. E.‐S.. 2024. “Nursing Students' Perception and Attitudes Toward Utilization of Artificial Intelligence in Health Care.” Edelweiss Applied Science and Technology 8, no. 6: 663–670. 10.55214/25768484.v8i6.2138. [ DOI ] [ Google Scholar ] Amechghal, A. , Naciri A., Takhdat K., Lahlou L., and Nassik H.. 2025. “Attitudes of Moroccan Nursing Students Toward Artificial Intelligence: Implications for Simulation‐Based Education.” Malque Health 1, no. 2: 2025005. 10.31893/health.2025005. [ DOI ] [ Google Scholar ] Amin, S. M. , El‐Gazar H. E., Zoromba M. A., El‐Sayed M. M., and Atta M. H. R.. 2025. “Sentiment of Nurses Towards Artificial Intelligence and Resistance to Change in Healthcare Organisations: A Mixed‐Method Study.” Journal of Advanced Nursing 81, no. 4: 2087–2098. 10.1111/jan.16435. [ DOI ] [ PubMed ] [ Google Scholar ] Ashwini, P. , and Padhy P.. 2023. “Impact of AI on Healthcare With Specific Reference to Nurses' Education.” Journal of Advanced Zoology 44, no. S‐5: 818–829. 10.17762/jaz.v44iS-5.1002. [ DOI ] [ Google Scholar ] Atalla, A. D. G. , El‐Ashry A. M., and Mohamed Sobhi Mohamed S.. 2024. “The Moderating Role of Ethical Awareness in the Relationship Between Nurses' Artificial Intelligence Perceptions, Attitudes, and Innovative Work Behavior: A Cross‐Sectional Study.” BMC Nursing 23, no. 1: 488. 10.1186/s12912-024-02143-0. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Bi, A. , Li T., Cheng G., and Hu J.. 2025. “Artificial Intelligence Applications in Intensive Care Unit Nursing: A Narrative Review (2020‐2025).” DIGITAL HEALTH 11: 20552076251406304. 10.1177/20552076251406302. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Bodur, G. , Cakir H., Turan S., Seren A. K. H., and Goktas P.. 2025. “Artificial Intelligence in Nursing Practice: A Qualitative Study of Nurses' Perspectives on Opportunities, Challenges, and Ethical Implications.” BMC Nursing 24, no. 1: 1263. 10.1186/s12912-025-03775-6. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Cooper, J. , Haroon S., Crowe F., et al. 2025. “Perspectives of Health Care Professionals on the Use of aı to Support Clinical Decision‐Making in the Management of Multiple Long‐Term Conditions: Interview Study.” Journal of Medical Internet Research 27: e71980. 10.2196/71980. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Coşkun, A. B. , Kenner C., and Elmaoğlu E.. 2024. “Neonatal Intensive Care Nurses' Perceptions of Artificial Intelligence.” Journal of Perinatal and Neonatal Nursing. 10.1097/JPN.0000000000000904. [ DOI ] [ PubMed ] [ Google Scholar ] Ecarnot, F. , Lombion S., Pourrez A., et al. 2022. “A Qualitative Study of the Perceptions and Experiences of Healthcare Providers Caring for Critically Ill Patients During the First Wave of the COVID‐19 Pandemic: A PsyCOVID‐ICU Substudy.” PLoS One 17, no. 9: e0274326. 10.1371/journal.pone.0274326. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Elhaddad, M. , and Hamam S.. 2024. “AI‐Driven Clinical Decision Support Systems: An Ongoing Pursuit of Potential.” Cureus 16: e57728. 10.7759/cureus.57728. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] El‐Sayed, A. A. I. , Alsenany S. A., Badoman T. E. A., and Asal M. G. R.. 2025. “Development and Validation of a Scale for Nurses' Ethical Awareness in the Use of Artificial Intelligence: A Methodological Study.” Nursing and Health Sciences 27, no. 2: e70140. 10.1111/nhs.70140. [ DOI ] [ PubMed ] [ Google Scholar ] Elsayed, W. , and Sleem W.. 2021. “Nurse Managers' Perspectives and Attitude Toward Using Artificial Intelligence Technology in Nursing Settings.” Assiut Scientific Nursing Journal 0, no. 0: 0–0. 10.21608/asnj.2021.72740.1159. [ DOI ] [ Google Scholar ] Erkayıran, O. , and Aslan R.. 2025. “Evaluation of Nurses' Perceptions and Readiness for Artificial Intelligence Integration in Healthcare: A Cross‐Sectional Study in Turkey.” Journal of Advanced Nursing: 1–13. 10.1111/jan.70256. [ DOI ] [ PubMed ] [ Google Scholar ] Esteva, A. , Kuprel B., Novoa R. A., et al. 2017. “Dermatologist‐Level Classification of Skin Cancer With Deep Neural Networks.” Nature 542, no. 7639: 115–118. 10.1038/nature21056. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Fawaz, M. , El‐Malti W., Alreshidi S. M., and Kavuran E.. 2025. “Exploring Health Sciences Students' Perspectives on Using Generative Artificial Intelligence in Higher Education: A Qualitative Study.” Nursing and Health Sciences 27, no. 1: e70030. 10.1111/nhs.70030. [ DOI ] [ PubMed ] [ Google Scholar ] Ganvir, P. M. , Chandekar D. P. A., Ranadive C., Kulkarni D. S. M., Suresh B. R., and Begum F.. 2025. “Artificial Intelligence in Clinical Nursing and Healthcare Systems: Innovation, Impact, and Responsibility.” International Journal of Environmental Sciences 11: 811–816. 10.64252/053w1f52. [ DOI ] [ Google Scholar ] Guest, G. , Bunce A., and Johnson L.. 2006. “How Many Interviews Are Enough?” Field Methods 18, no. 1: 59–82. 10.1177/1525822X05279903. [ DOI ] [ Google Scholar ] Gumus, E. , and Alan H.. 2025. “Perspectives of Physicians, Nurses, and Patients on the Use of Artificial Intelligence and Robotic Nurses in Healthcare.” International Nursing Review 72, no. 2: e70017. 10.1111/inr.70017. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Jeyaraman, M. , Balaji S., Jeyaraman N., and Yadav S.. 2023. “Unraveling the Ethical Enigma: Artificial Intelligence in Healthcare.” Cureus 15: e43262. 10.7759/cureus.43262. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Jiang, F. , Jiang Y., Zhi H., et al. 2017. “Artificial Intelligence in Healthcare: Past, Present and Future.” Stroke and Vascular Neurology 2, no. 4: 230–243. 10.1136/svn-2017-000101. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Kahraman, H. , Akutay S., Yüceler Kaçmaz H., and Taşci S.. 2025. “Artificial Intelligence Literacy Levels of Perioperative Nurses: The Case of Türkiye.” Nursing and Health Sciences 27, no. 1: e70059. 10.1111/nhs.70059. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Lora, L. , and Fo P.. 2025. “Nurses' Perceptions of Artificial Intelligence (AI) Integration Into Practice: An Integrative Review.” Journal of Perioperative Nursing 37, no. 3: e22–e28. 10.26550/2209-1092.1366. [ DOI ] [ Google Scholar ] Mohamed Abd El‐Monem, A. , Elsayed Rashed S., and Ghoneimy Hasanin A.. 2023. “Artificial Intelligence Technology and Its Relation to Staff Nurses' Professional Identity and Problem Solving Abilities.” International Egyptian Journal of Nursing Sciences and Research 3, no. 2: 144–164. 10.21608/ejnsr.2023.277890. [ DOI ] [ Google Scholar ] Nashwan, A. J. , Cabrega J. A., Othman M. I., et al. 2025. “The Evolving Role of Nursing Informatics in the Era of Artificial Intelligence.” International Nursing Review 72, no. 1: e13084. 10.1111/inr.13084. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Noy, C.
2008. “Sampling Knowledge: The Hermeneutics of Snowball Sampling in Qualitative Research.” International Journal of Social Research Methodology 11, no. 4: 327–344. 10.1080/13645570701401305. [ DOI ] [ Google Scholar ] O'Brien, B. C. , Harris I. B., Beckman T. J., Reed D. A., and Cook D. A.. 2014. “Standards for Reporting Qualitative Research.” Academic Medicine 89, no. 9: 1245–1251. 10.1097/ACM.0000000000000388. [ DOI ] [ PubMed ] [ Google Scholar ] Porcellato, E. , Lanera C., Ocagli H., and Danielis M.. 2025. “Exploring Applications of Artificial Intelligence in Critical Care Nursing: A Systematic Review.” Nursing Reports 15, no. 2: 55. 10.3390/nursrep15020055. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Sabra, H. , Abd Elaal H., Sobhy K., and Bakr M.. 2023. “Utilization of Artificial Intelligence in Health Care: Nurses' Perspectives and Attitudes.” Menoufia Nursing Journal 8, no. 1: 243–257. 10.21608/menj.2023.297411. [ DOI ] [ Google Scholar ] Saunders, B. , Sim J., Kingstone T., et al. 2018. “Saturation in Qualitative Research: Exploring Its Conceptualization and Operationalization.” Quality and Quantity 52, no. 4: 1893–1907. 10.1007/s11135-017-0574-8. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Thorne, S.
2016. Interpretive Description. Routledge. 10.4324/9781315545196. [ DOI ] [ Google Scholar ] Topol, E. J.
2019. “High‐Performance Medicine: The Convergence of Human and Artificial Intelligence.” Nature Medicine 25, no. 1: 44–56. 10.1038/s41591-018-0300-7. [ DOI ] [ PubMed ] [ Google Scholar ] Tuncer, G. Z. , and Tuncer M.. 2024. “Investigation of Nurses' General Attitudes Toward Artificial Intelligence and Their Perceptions of ChatGPT Usage and Influencing Factors.” Dıgıtal Health 10: 20552076241277025. 10.1177/20552076241277025. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Vasquez, B. A. , Moreno‐Lacalle R., Soriano G. P., Juntasoopeepun P., Locsin R. C., and Evangelista L. S.. 2023. “Technological Machines and Artificial Intelligence in Nursing Practice.” Nursing and Health Sciences 25, no. 3: 474–481. 10.1111/nhs.13029. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Vernic, C. , Tamas T. P., Petre I., and Ursoniu S.. 2025. “Transforming Critical Care: The Digital Revolution's Impact on Intensive Care Units.” Frontiers in Digital Health 7: 1664382. 10.3389/fdgth.2025.1664382. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Data Availability Statement De‐identified interview transcripts and study materials are available from the corresponding author upon reasonable request. Articles from Nursing & Health Sciences are provided here courtesy of Wiley ACTIONS View on publisher site PDF (547.8 KB) Cite Collections Permalink PERMALINK Copy RESOURCES Similar articles Cited by other articles Links to NCBI Databases Cite Copy Download .nbib .nbib Format: AMA APA MLA NLM Add to Collections Create a new collection Add to an existing collection Name your collection * Choose a collection Unable to load your collection due to an error Please try again Add Cancel Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top