From Carb Counting to Diagnosis: Real World Patient Uses and Attitudes Toward Large Language Models in Diabetes Management

From Carb Counting to Diagnosis: Real World Patient Uses and Attitudes Toward Large Language Models in Diabetes Management | medRxiv Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search From Carb Counting to Diagnosis: Real World Patient Uses and Attitudes Toward Large Language Models in Diabetes Management Raisa Ntemenyi Nkweteyim , Vishesh Girish Shet , Sandra Iregbu , Lu He doi: https://doi.org/10.64898/2026.03.10.26348079 Raisa Ntemenyi Nkweteyim 1 College of Engineering and Applied Science, University of Wisconsin-Milwaukee , Milwaukee, Wisconsin MS Find this author on Google Scholar Find this author on PubMed Search for this author on this site Vishesh Girish Shet 2 School of Information Studies, University of Wisconsin-Milwaukee , Milwaukee, Wisconsin Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sandra Iregbu 3 School of Nursing, University of Wisconsin-Milwaukee , Milwaukee, Wisconsin PhD, RN Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lu He 4 Zilber College of Public Health, University of Wisconsin-Milwaukee , Milwaukee, Wisconsin PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: he32{at}uwm.edu Abstract Full Text Info/History Metrics Data/Code Preview PDF ABSTRACT Managing diabetes-related conditions is time-intensive and cognitively demanding for patients and caregivers, requiring ongoing glucose monitoring, dietary regulation, physical activity planning, and continuous lifestyle adaptation. With the emergence of large language models (LLMs), patients have increasingly turned to these tools for information, guidance, and support. However, there is limited empirical understanding of which diabetes-related medical tasks patients delegate to LLMs and what their experiences are. To address this gap, we combined qualitative thematic analysis with LLM-assisted analysis to examine patient attitudes and real-world use cases in using LLMs for diabetes-related tasks. Our analysis identified diverse application areas, ranging from clinical interpretation to nutrition and diet support, and disease management amongst others. LLMs functioned not only as information sources, but as interpretive, analytical, decision-support, emotional, and logistical aids supporting patients’ self-management. Last, we discuss implications for integrating LLMs into patients’ self-management support ecosystems and identify areas that require support and safeguards. Competing Interest Statement The authors have declared no competing interest. Funding Statement No funding was received for this study. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability Data will be provided at reasonable request to the author. Copyright The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license . Back to top Previous Next Posted March 19, 2026. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. 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