{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T21:29:55Z","timestamp":1786483795227,"version":"build-2736575974"},"reference-count":107,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Med Internet Res"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec sec-type=\"background\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Chronic diseases account for nearly three-quarters of global deaths and demand continuous, personalized long-term management; yet traditional care models often fall short in delivering such sustained support. Large language models, with advanced conversational and analytical capabilities, present promising opportunities to address the problem by offering scalable, interactive support. However, a comprehensive synthesis of evidence across diverse study designs, which moves beyond isolated technical metrics to evaluate large language models through a structured, theory-driven lens, remains limited.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"objective\">\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to synthesize quantitative, qualitative, and mixed methods evidence on technical performance, application scenarios, and documented challenges of large language models in chronic disease care, and to critically evaluate these findings through a theory-driven, 3D framework informed by Orem\u2019s Self-Care Theory.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The mixed methods systematic review adhered to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 and SWiM (Synthesis Without Meta-Analysis) guidelines. A comprehensive computer-based search was conducted across PubMed, Web of Science, Embase, Cochrane Library, CINAHL, Wiley Online Library, SpringerLink, ScienceDirect, China National Knowledge Infrastructure, Wanfang Data, VIP Database, and the China Biology Medicine Disc from inception to April 2026. Two independent researchers performed study screening, data extraction, and quality appraisal using the Mixed Methods Appraisal Tool 2018. Given significant clinical and methodological heterogeneity across the included studies, a quantitative meta-analysis was not appropriate; instead, thematic synthesis was used following Thomas and Harden\u2019s 3-step approach, with NVivo 14 (Lumivero) used for line-by-line coding and theme development, all informed by Orem\u2019s 3D framework.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 20 studies were included, all rated as moderate or high quality. The thematic synthesis revealed three core themes aligned with the proposed framework: (1) foundational safety, privacy, and fairness (hallucination risks and data concerns); (2) self-care enablement through perceived usefulness (patient education, decision support, and self-management); and (3) design and system integration challenges (readability mismatches and workflow gaps). Patient education and clinical decision support were the most common application scenarios. Key technical enhancements (retrieval-augmented generation [RAG] and fine-tuning) primarily strengthened the second theme, while barriers such as content readability, hallucination risks, and ethical ambiguities limited real-world readiness.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This review is the first to integrate Orem\u2019s Self-Care Theory into a 3D evaluative framework for large language models in chronic care, thereby moving beyond fragmented, technology-centric assessments toward a structured, nursing-informed, and theory-driven synthesis. Unlike prior reviews that primarily focused on isolated technical metrics or broad feasibility, this synthesis provides a layered, discipline-grounded evaluation distinguishing foundational safety, self-care enablement, and system integration. These findings show evidence mainly supports the intermediate layer, while safeguards and operational integration remain deficient, guiding nursing research toward safety assurances, health-literacy-adaptive design, and implementation science for equitable, patient-centered care.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/90744","type":"journal-article","created":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T20:51:32Z","timestamp":1786481492000},"page":"e90744-e90744","source":"Crossref","is-referenced-by-count":0,"title":["Application of Large Language Models in Chronic Disease Care: Mixed Methods Systematic Review and Thematic Synthesis"],"prefix":"10.2196","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-7485-0026","authenticated-orcid":false,"given":"Linghui","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2782-001X","authenticated-orcid":false,"given":"Panpan","family":"Huai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0549-6613","authenticated-orcid":false,"given":"Rui","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6381-3015","authenticated-orcid":false,"given":"Jingjing","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8474-3998","authenticated-orcid":false,"given":"Ruihua","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8466-9126","authenticated-orcid":false,"given":"Huimei","family":"Lv","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,8,11]]},"reference":[{"issue":"1","key":"R1","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1097\/CM9.0000000000003898","article-title":"Non-communicable disease burden in China, 1990-2023: evidence from the Global Burden of Disease Study 2023","volume":"139","author":"Zhang","journal-title":"Chin Med J (Engl)"},{"key":"R2","doi-asserted-by":"publisher","DOI":"10.1016\/j.lanepe.2025.101361","article-title":"Advancing Europe\u2019s non-communicable diseases agenda through cross-national collaboration: translating WHO-Europe findings into actionable strategies","volume":"55","author":"Armocida","journal-title":"Lancet Reg Health Eur"},{"issue":"10255","key":"R3","doi-asserted-by":"publisher","first-page":"918","DOI":"10.1016\/S0140-6736(20)31761-X","article-title":"NCD Countdown 2030: pathways to achieving Sustainable Development Goal target 3.4","volume":"396","author":"NCD Countdown 2030 collaborators","journal-title":"Lancet"},{"issue":"10513","key":"R4","doi-asserted-by":"publisher","first-page":"1873","DOI":"10.1016\/S0140-6736(25)01637-X","article-title":"Burden of 375 diseases and injuries, risk-attributable burden of 88 risk factors, and healthy life expectancy in 204 countries and territories, including 660 subnational locations, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023","volume":"406","author":"GBD 2023 Disease and Injury and Risk Factor Collaborators","journal-title":"Lancet"},{"key":"R5","doi-asserted-by":"publisher","DOI":"10.2147\/IJGM.S516247","article-title":"Artificial intelligence in chronic disease management for aging populations: a systematic review of machine learning and NLP applications","volume":"18","author":"Feng","journal-title":"Int J Gen Med"},{"issue":"1","key":"R6","doi-asserted-by":"publisher","DOI":"10.1186\/s12913-024-11351-y","article-title":"Care models for individuals with chronic multimorbidity: lessons for low- and middle-income countries","volume":"24","author":"Endalamaw","journal-title":"BMC Health Serv Res"},{"issue":"1","key":"R7","doi-asserted-by":"publisher","DOI":"10.1186\/s12913-026-14175-0","article-title":"Barriers and facilitators to the implementation and scale-up of a mHealth integrated care program for diabetes and hypertension in Ghana: a qualitative study of the Akoma Pa program","volume":"26","author":"Struss","journal-title":"BMC Health Serv Res"},{"issue":"1","key":"R8","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-025-67268-5","article-title":"Worldwide trends in metabolic syndrome from 2000 to 2023: a systematic review and modelling analysis","volume":"17","author":"Noubiap","journal-title":"Nat Commun"},{"key":"R9","doi-asserted-by":"publisher","DOI":"10.1016\/j.metabol.2026.156575","article-title":"Burden of metabolic diseases, 1990-2023, with forecasts to 2030 for the Asia-Pacific region","volume":"179","author":"Zhang","journal-title":"Metabolism"},{"key":"R10","doi-asserted-by":"publisher","DOI":"10.1177\/20552076251344375","article-title":"A comprehensive digital medicine platform for hypertension and diabetes care in primary care: a real-world feasibility test","volume":"11","author":"Bae","journal-title":"Digit Health"},{"issue":"1","key":"R11","doi-asserted-by":"publisher","DOI":"10.1186\/s44263-025-00236-9","article-title":"Understanding digital health literacy as a structural determinant of health and public health capability","volume":"4","author":"Wamala-Andersson","journal-title":"BMC Glob Public Health"},{"issue":"1","key":"R12","doi-asserted-by":"publisher","DOI":"10.1186\/s12967-025-07201-y","article-title":"Implementation of precision medicine in treating non-communicable diseases: a systematic review","volume":"23","author":"Liang","journal-title":"J Transl Med"},{"issue":"10","key":"R13","doi-asserted-by":"publisher","first-page":"761","DOI":"10.1038\/s41415-023-5845-2","article-title":"Artificial intelligence in healthcare and education","volume":"234","author":"Dave","journal-title":"Br Dent J"},{"issue":"20","key":"R14","doi-asserted-by":"publisher","first-page":"2051","DOI":"10.1016\/j.jacc.2024.08.069","article-title":"Artificial intelligence in cardiovascular clinical trials","volume":"84","author":"Cunningham","journal-title":"J Am Coll Cardiol"},{"issue":"2","key":"R15","doi-asserted-by":"publisher","DOI":"10.1111\/jebm.70031","article-title":"Large language models in integrative medicine: progress, challenges, and opportunities","volume":"18","author":"Yip","journal-title":"J Evid Based Med"},{"issue":"1","key":"R16","doi-asserted-by":"publisher","DOI":"10.1186\/s12903-025-07035-6","article-title":"Cross-lingual performance of large language models in maxillofacial prosthodontics: a comparative evaluation","volume":"25","author":"Sozen Yanik","journal-title":"BMC Oral Health"},{"issue":"2","key":"R17","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/j.oret.2023.09.008","article-title":"The use of large language models to generate education materials about uveitis","volume":"8","author":"Kianian","journal-title":"Ophthalmol Retina"},{"key":"R18","doi-asserted-by":"publisher","DOI":"10.2147\/AMEP.S497020","article-title":"Delving into the practical applications and pitfalls of large language models in medical education: narrative review","volume":"16","author":"Li","journal-title":"Adv Med Educ Pract"},{"issue":"3","key":"R19","doi-asserted-by":"publisher","DOI":"10.1111\/1753-0407.70072","article-title":"Comparative analysis of AI tools for disseminating ADA 2025 Diabetes Care Standards: implications for cardiovascular physicians","volume":"17","author":"Zheng","journal-title":"J Diabetes"},{"key":"R20","doi-asserted-by":"publisher","DOI":"10.2196\/77948","article-title":"Large language models in nursing education: concept analysis","volume":"8","author":"Harrington","journal-title":"JMIR Nurs"},{"issue":"2","key":"R21","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2024.57879","article-title":"Large language models for chatbot health advice studies: a systematic review","volume":"8","author":"Huo","journal-title":"JAMA Netw Open"},{"issue":"6","key":"R22","doi-asserted-by":"publisher","first-page":"390","DOI":"10.1093\/dmfr\/twae021","article-title":"How well do large language model-based chatbots perform in oral and maxillofacial radiology?","volume":"53","author":"Jeong","journal-title":"Dentomaxillofac Radiol"},{"key":"R23","doi-asserted-by":"publisher","DOI":"10.2196\/65984","article-title":"Large language model applications for health information extraction in oncology: scoping review","volume":"11","author":"Chen","journal-title":"JMIR Cancer"},{"key":"R24","doi-asserted-by":"publisher","DOI":"10.2196\/66905","article-title":"Using large language models for chronic disease management tasks: scoping review","volume":"13","author":"Serugunda","journal-title":"JMIR Med Inform"},{"issue":"1","key":"R25","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-025-01963-x","article-title":"The evaluation illusion of large language models in medicine","volume":"8","author":"Agrawal","journal-title":"NPJ Digit Med"},{"key":"R26","doi-asserted-by":"publisher","DOI":"10.2196\/64963","article-title":"Comparing diagnostic accuracy of clinical professionals and large language models: systematic review and meta-analysis","volume":"13","author":"Shan","journal-title":"JMIR Med Inform"},{"issue":"3","key":"R27","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.80737","article-title":"Evaluating the performance and safety of large language models in generating type 2 diabetes mellitus management plans: a comparative study with physicians using real patient records","volume":"17","author":"Mondal","journal-title":"Cureus"},{"key":"R28","doi-asserted-by":"publisher","DOI":"10.1016\/j.nut.2025.112957","article-title":"Comparison of ChatGPT-3.5, ChatGPT-4.0 and DeepSeek in generating dietary plans for patients with chronic kidney disease: a focus on nutritional accuracy and dietary inflammation","volume":"142","author":"You","journal-title":"Nutrition"},{"issue":"11","key":"R29","doi-asserted-by":"publisher","first-page":"2792","DOI":"10.7150\/ijms.111780","article-title":"Large language models in medicine: applications, challenges, and future directions","volume":"22","author":"Yu","journal-title":"Int J Med Sci"},{"key":"R30","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2026.1805848","article-title":"Performance evaluation of mainstream large language models in autoimmune hepatitis patient education: a comparative study of readability, quality, and reliability","volume":"14","author":"Li","journal-title":"Front Public Health"},{"issue":"1","key":"R31","doi-asserted-by":"publisher","DOI":"10.1038\/s43856-025-00927-2","article-title":"Evaluation of a large language model to simplify discharge summaries and provide cardiological lifestyle recommendations","volume":"5","author":"Rust","journal-title":"Commun Med (Lond)"},{"issue":"1","key":"R32","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare14010045","article-title":"Large language models for cardiovascular disease, cancer, and mental disorders: a review of systematic reviews","volume":"14","author":"Triantafyllidis","journal-title":"Healthcare (Basel)"},{"key":"R33","doi-asserted-by":"publisher","DOI":"10.2196\/70535","article-title":"Unveiling the potential of large language models in transforming chronic disease management: mixed methods systematic review","volume":"27","author":"Li","journal-title":"J Med Internet Res"},{"key":"R34","doi-asserted-by":"publisher","DOI":"10.1111\/jocn.70151","article-title":"Generative AI at the bedside: an integrative review of applications and implications in clinical nursing practice","author":"Watson","journal-title":"J Clin Nurs"},{"issue":"1","key":"R35","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-026-02492-x","article-title":"A scalable framework for evaluating health language models","volume":"9","author":"Mallinar","journal-title":"NPJ Digit Med"},{"key":"R36","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2024.105501","article-title":"Integrating human expertise & automated methods for a dynamic and multi-parametric evaluation of large language models\u2019 feasibility in clinical decision-making","volume":"188","author":"Sblendorio","journal-title":"Int J Med Inform"},{"issue":"1","key":"R37","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1111\/inr.12808","article-title":"Application of Orem\u2019s theory-based caring programs among chronically ill adults: a systematic review and dose-response meta-analysis","volume":"70","author":"Nasiri","journal-title":"Int Nurs Rev"},{"issue":"1","key":"R38","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1177\/08943184211051369","article-title":"Orem\u2019s Self-Care Deficit Nursing Theory: relevance and need for refinement","volume":"35","author":"Hartweg","journal-title":"Nurs Sci Q"},{"issue":"8","key":"R39","doi-asserted-by":"publisher","first-page":"767","DOI":"10.17219\/acem\/191102","article-title":"Self-care: an effective strategy to manage chronic diseases","volume":"33","author":"Iovino","journal-title":"Adv Clin Exp Med"},{"key":"R40","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 statement: an updated guideline for reporting systematic reviews","volume":"372","author":"Page","journal-title":"BMJ"},{"issue":"1","key":"R41","doi-asserted-by":"publisher","DOI":"10.1186\/s13643-020-01542-z","article-title":"PRISMA-S: an extension to the PRISMA statement for reporting literature searches in systematic reviews","volume":"10","author":"Rethlefsen","journal-title":"Syst Rev"},{"issue":"1","key":"R42","doi-asserted-by":"publisher","DOI":"10.1186\/s12875-025-02785-2","article-title":"Opportunities, challenges, and requirements for artificial intelligence (AI) implementation in Primary Health Care (PHC): a systematic review","volume":"26","author":"Yousefi","journal-title":"BMC Prim Care"},{"issue":"2","key":"R43","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1123\/jpah.2025-0895","article-title":"The Political Declaration of the Fourth United Nations High-Level Meeting on Noncommunicable Diseases and Mental Health-Implications for Physical Activity Promotion","volume":"23","author":"Milton","journal-title":"J Phys Act Health"},{"issue":"3","key":"R44","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1111\/jep.12884","article-title":"Improving the usefulness of a tool for appraising the quality of qualitative, quantitative and mixed methods studies, the Mixed Methods Appraisal Tool (MMAT)","volume":"24","author":"Hong","journal-title":"J Eval Clin Pract"},{"issue":"6","key":"R45","doi-asserted-by":"publisher","first-page":"902","DOI":"10.1016\/j.fertnstert.2023.03.015","article-title":"When to pool data in a meta-analysis (and when not to)?","volume":"119","author":"Lensen","journal-title":"Fertil Steril"},{"key":"R46","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.l6890","article-title":"Synthesis without meta-analysis (SWiM) in systematic reviews: reporting guideline","volume":"368","author":"Campbell","journal-title":"BMJ"},{"key":"R47","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2288-8-45","article-title":"Methods for the thematic synthesis of qualitative research in systematic reviews","volume":"8","author":"Thomas","journal-title":"BMC Med Res Methodol"},{"issue":"12","key":"R48","doi-asserted-by":"publisher","first-page":"12","DOI":"10.3390\/diagnostics15121455","article-title":"Evaluation of the performance of large language models in the management of axial spondyloarthropathy: analysis of EULAR 2022 Recommendations","volume":"15","author":"Usen","journal-title":"Diagnostics (Basel)"},{"key":"R49","doi-asserted-by":"publisher","DOI":"10.2196\/65903","article-title":"Exploring the capacity of large language models to assess the chronic pain experience: algorithm development and validation","volume":"27","author":"Amidei","journal-title":"J Med Internet Res"},{"issue":"3","key":"R50","doi-asserted-by":"publisher","first-page":"101092","DOI":"10.3748\/wjg.v31.i3.101092","article-title":"Exploring the performance of large language models on hepatitis B infection-related questions: a comparative study","volume":"31","author":"Li","journal-title":"World J Gastroenterol"},{"issue":"10","key":"R51","doi-asserted-by":"publisher","DOI":"10.1111\/liv.70349","article-title":"From guidelines to real-time conversation: expert-validated retrieval-augmented and fine-tuned GPT-4 for hepatitis C management","volume":"45","author":"Giuffr\u00e8","journal-title":"Liver Int"},{"key":"R52","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2025.104885","article-title":"GutGPT: a multidimensional knowledge-enhanced large language model for gastrointestinal medicine","volume":"169","author":"Zhang","journal-title":"J Biomed Inform"},{"key":"R53","doi-asserted-by":"publisher","DOI":"10.2196\/66365","article-title":"Identifying kidney stone risk factors through patient experiences with a large language model: text analysis and empirical study","volume":"27","author":"Mao","journal-title":"J Med Internet Res"},{"key":"R54","doi-asserted-by":"publisher","DOI":"10.2196\/77053","article-title":"Interdisciplinary development and fine-tuning of CARDIO, a large language model for cardiovascular health education in HIV care: tutorial","volume":"27","author":"Rullo","journal-title":"J Med Internet Res"},{"issue":"1","key":"R55","doi-asserted-by":"publisher","DOI":"10.1136\/bmjpo-2025-003742","article-title":"Leveraging large language models to inform paediatric chronic condition care: a cross-sectional study","volume":"9","author":"Jamil","journal-title":"BMJ Paediatr Open"},{"key":"R56","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2025.3577604","article-title":"Lightweight LLM-based anomaly detection framework for securing IoTMD enabled diabetes management control systems","volume":"PP","author":"Pawana","journal-title":"IEEE J Biomed Health Inform"},{"issue":"1","key":"R57","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-025-01969-5","article-title":"Optimizing large language models for detecting symptoms of depression\/anxiety in chronic diseases patient communications","volume":"8","author":"Kim","journal-title":"NPJ Digit Med"},{"key":"R58","doi-asserted-by":"publisher","DOI":"10.2196\/71418","article-title":"Perceptions and attitudes of Chinese oncologists toward endorsing AI-driven chatbots for health information seeking among patients with cancer: phenomenological qualitative study","volume":"27","author":"Zeng","journal-title":"J Med Internet Res"},{"issue":"1","key":"R59","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-84003-0","article-title":"A case study on using a large language model to analyze continuous glucose monitoring data","volume":"15","author":"Healey","journal-title":"Sci Rep"},{"issue":"2","key":"R60","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1038\/s41591-025-04190-9","article-title":"A large language model for complex cardiology care","volume":"32","author":"O\u2019Sullivan","journal-title":"Nat Med"},{"issue":"1","key":"R61","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1007\/s41666-025-00217-5","article-title":"Assessing the user experience of an LLM-based conversational assistant in diabetes mellitus care","volume":"10","author":"Furtado","journal-title":"J Healthc Inform Res"},{"key":"R62","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2026.1777577","article-title":"Decoupled quality and readability in skin cancer education from large language models","volume":"14","author":"Zhang","journal-title":"Front Public Health"},{"issue":"24","key":"R63","doi-asserted-by":"publisher","DOI":"10.3390\/nu17243828","article-title":"Efficacy of large language models in providing evidence-based patient education for celiac disease: a comparative analysis","volume":"17","author":"Bertin","journal-title":"Nutrients"},{"key":"R64","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2025.116168","article-title":"Enhancing clinicians\u2019 trust in large language models via transparent source attribution: a randomized controlled evaluation in uro-oncology","volume":"233","author":"Carl","journal-title":"Eur J Cancer"},{"issue":"1","key":"R65","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-32373-4","article-title":"Interpretable glucose forecasting for type 2 diabetes across traditional, deep, and large language models","volume":"16","author":"Alredaini","journal-title":"Sci Rep"},{"issue":"3","key":"R66","doi-asserted-by":"publisher","first-page":"943","DOI":"10.1016\/j.jtha.2025.09.004","article-title":"Large language models vs thrombosis experts: a comparative study on patient education and clinical decision-making in venous thromboembolism","volume":"24","author":"Vladic","journal-title":"Journal of Thrombosis and Haemostasis"},{"issue":"1","key":"R67","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-27235-y","article-title":"Multidimensional assessment of large language model responses to patient questions on gestational diabetes mellitus","volume":"15","author":"Yigit Yalc\u0131n","journal-title":"Sci Rep"},{"key":"R68","doi-asserted-by":"publisher","DOI":"10.2196\/89831","article-title":"Cross-sectional evaluation of medical disinformation safeguards in consumer-facing large language model platforms","volume":"6","author":"Modi","journal-title":"JMIR Infodemiology"},{"issue":"7","key":"R69","doi-asserted-by":"publisher","DOI":"10.1097\/CIN.0000000000001481","article-title":"Bridging the gap between potential and practice: an integrative review of generative artificial intelligence in nursing","volume":"44","author":"Campbell","journal-title":"Comput Inform Nurs"},{"issue":"10152","key":"R70","doi-asserted-by":"publisher","first-page":"1072","DOI":"10.1016\/S0140-6736(18)31992-5","article-title":"NCD Countdown 2030: worldwide trends in non-communicable disease mortality and progress towards Sustainable Development Goal target 3.4","volume":"392","author":"NCD Countdown 2030 collaborators","journal-title":"Lancet"},{"issue":"12","key":"R71","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0336036","article-title":"Global burden and future projections of non-communicable diseases (2000-2050): progress toward SDG 3.4 and disparities across regions and risk factors","volume":"20","author":"Freihat","journal-title":"PLoS One"},{"issue":"9","key":"R72","doi-asserted-by":"publisher","DOI":"10.1093\/ckj\/sfaf243","article-title":"Clinical applications and limitations of large language models in nephrology: a systematic review","volume":"18","author":"Unger","journal-title":"Clin Kidney J"},{"issue":"5","key":"R73","doi-asserted-by":"publisher","DOI":"10.21037\/atm-25-62","article-title":"Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review","volume":"13","author":"Osonuga","journal-title":"Ann Transl Med"},{"issue":"1","key":"R74","doi-asserted-by":"publisher","DOI":"10.1186\/s12931-025-03438-9","article-title":"Accuracy, comprehensiveness and understandability of AI-generated answers to questions from people with COPD: the AIR-COPD Study","volume":"27","author":"Nigro","journal-title":"Respir Res"},{"key":"R75","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoenv.2025.118220","article-title":"Non-targeted analysis of odor components and hazardous volatiles in children\u2019s raincoats","volume":"296","author":"Shi","journal-title":"Ecotoxicol Environ Saf"},{"issue":"7","key":"R76","doi-asserted-by":"publisher","DOI":"10.1161\/HYPERTENSIONAHA.126.27004","article-title":"Empowering patients and clinicians: LLMs in hypertension care, a scoping review","volume":"83","author":"Pariente","journal-title":"Hypertension"},{"key":"R77","doi-asserted-by":"publisher","DOI":"10.2196\/84918","article-title":"Factors influencing adoption of large language models in health care: multicenter cross-sectional mixed methods observational study","volume":"27","author":"Yang","journal-title":"J Med Internet Res"},{"issue":"4","key":"R78","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1111\/jpm.70137","article-title":"Optimisation drift and substitution risk in artificial intelligence-supported personalised mental health nursing: a critical synthesis on therapeutic presence and care biography","volume":"33","author":"Y\u0131ld\u0131z","journal-title":"J Psychiatr Ment Health Nurs"},{"key":"R79","doi-asserted-by":"publisher","DOI":"10.2196\/75849","article-title":"Harm reduction strategies for thoughtful use of large language models in the medical domain: perspectives for patients and clinicians","volume":"27","author":"Mo\u00ebll","journal-title":"J Med Internet Res"},{"key":"R80","doi-asserted-by":"publisher","DOI":"10.2196\/65523","article-title":"Nurse researchers\u2019 experiences and perceptions of generative AI: qualitative semistructured interview study","volume":"27","author":"Kang","journal-title":"J Med Internet Res"},{"key":"R81","doi-asserted-by":"publisher","DOI":"10.1016\/j.lanwpc.2025.101707","article-title":"Large language models and global health equity: a roadmap for equitable adoption in LMICs","volume":"63","author":"Chen","journal-title":"Lancet Reg Health West Pac"},{"key":"R82","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpeds.2026.115037","article-title":"Large language models for maternal and neonatal health care in low- and middle-income countries","volume":"293","author":"Yu","journal-title":"J Pediatr"},{"key":"R83","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2025.1516088","article-title":"Contextual challenges in implementing artificial intelligence for healthcare in low-resource environments: insights from the SPEC-AI Nigeria trial","volume":"12","author":"Adedinsewo","journal-title":"Front Cardiovasc Med"},{"issue":"1191","key":"R84","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1093\/postmj\/qgae122","article-title":"Bridging healthcare gaps: a scoping review on the role of artificial intelligence, deep learning, and large language models in alleviating problems in medical deserts","volume":"101","author":"Strika","journal-title":"Postgrad Med J"},{"key":"R85","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2026.1737532","article-title":"An auditable and source-verified framework for clinical AI decision support: integrating retrieval-augmented generation with data provenance","volume":"9","author":"Alu","journal-title":"Front Artif Intell"},{"issue":"1","key":"R86","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-025-02297-7","article-title":"RAG-Enhanced open SLMs for hypertension management chatbots","volume":"49","author":"Aguzzi","journal-title":"J Med Syst"},{"key":"R87","doi-asserted-by":"publisher","DOI":"10.2196\/79379","article-title":"Evaluating web retrieval-assisted large language models with and without whitelisting for evidence-based neurology: comparative study","volume":"27","author":"Masanneck","journal-title":"J Med Internet Res"},{"issue":"3","key":"R88","doi-asserted-by":"publisher","first-page":"943","DOI":"10.1038\/s41591-024-03423-7","article-title":"Toward expert-level medical question answering with large language models","volume":"31","author":"Singhal","journal-title":"Nat Med"},{"key":"R89","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2026.1801829","article-title":"Challenges of using generative AI for patient education in chronic heart failure: an evaluation of content quality, readability, and actionability in cross-platform LLM-generated texts","volume":"14","author":"Wang","journal-title":"Front Public Health"},{"issue":"4","key":"R90","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1002\/cdt3.70020","article-title":"Evaluating the readability of AI-generated patient information on chronic diseases","volume":"11","author":"Shafau","journal-title":"Chronic Dis Transl Med"},{"issue":"6","key":"R91","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhsg.2025.100822","article-title":"Artificial intelligence can answer postoperative questions about distal radius fractures\u2014but can patients understand the answers?","volume":"7","author":"Tarapore","journal-title":"J Hand Surg Glob Online"},{"key":"R92","doi-asserted-by":"publisher","DOI":"10.3389\/fdgth.2025.1653631","article-title":"A systematic review of ethical considerations of large language models in healthcare and medicine","volume":"7","author":"Fareed","journal-title":"Front Digit Health"},{"key":"R93","doi-asserted-by":"publisher","DOI":"10.2147\/PPA.S527922","article-title":"Generative AI\/LLMs for plain language medical information for patients, caregivers and general public: opportunities, risks and ethics","volume":"19","author":"Pal","journal-title":"Patient Prefer Adher"},{"issue":"9","key":"R94","doi-asserted-by":"publisher","DOI":"10.1200\/CCI-25-00076","article-title":"Mitigating ethical issues for large language models in oncology: a systematic review","volume":"9","author":"Zhou","journal-title":"JCO Clin Cancer Inform"},{"issue":"1","key":"R95","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01157-x","article-title":"The ethics of ChatGPT in medicine and healthcare: a systematic review on large language models (LLMs)","volume":"7","author":"Haltaufderheide","journal-title":"NPJ Digit Med"},{"issue":"1","key":"R96","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1186\/s12886-025-04372-6","article-title":"A comprehensive overview: deep learning approaches to central serous chorioretinopathy diagnosis","volume":"25","author":"Shojaeinia","journal-title":"BMC Ophthalmol"},{"issue":"4","key":"R97","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1111\/jnu.70016","article-title":"The overlooked dark side of generative AI in nursing: an international think tank\u2019s perspective","volume":"57","author":"Topaz","journal-title":"J of Nursing Scholarship"},{"key":"R98","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2025.1504805","article-title":"Bridging the gap: a practical step-by-step approach to warrant safe implementation of large language models in healthcare","volume":"8","author":"Workum","journal-title":"Front Artif Intell"},{"issue":"1","key":"R99","doi-asserted-by":"publisher","DOI":"10.1136\/bmjhci-2025-101921","article-title":"Self-regulating the use of large language models in clinical practice: a risk-stratified approach","volume":"33","author":"Mohammad","journal-title":"BMJ Health Care Inform"},{"issue":"2","key":"R100","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1038\/s44360-025-00021-w","article-title":"A community-codesigned LLM-powered chatbot for primary care: a randomized controlled trial","volume":"1","author":"Li","journal-title":"Nat Health"},{"issue":"3","key":"R101","doi-asserted-by":"publisher","first-page":"1152","DOI":"10.1038\/s41591-026-04229-5","article-title":"LLM-assisted systematic review of large language models in clinical medicine","volume":"32","author":"Chen","journal-title":"Nat Med"},{"key":"R102","doi-asserted-by":"publisher","DOI":"10.2196\/81604","article-title":"Integrating a large language model to streamline nursing handover documentation across multiple hospitals in Taiwan: development and implementation study","volume":"28","author":"Chen","journal-title":"J Med Internet Res"},{"issue":"1","key":"R103","doi-asserted-by":"publisher","first-page":"907","DOI":"10.1111\/jan.16911","article-title":"An AI-enabled nursing future with no documentation burden: a vision for a new reality","volume":"82","author":"Michalowski","journal-title":"J Adv Nurs"},{"key":"R104","doi-asserted-by":"crossref","unstructured":"Tran H Yao Z Jang WS et al. MedReadCtrl: personalizing medical text generation with readability-controlled instruction learning. medRxiv. Preprint posted online on Jul 11, 2025. [doi: 10.1101\/2025.07.09.25331239] [Medline: 40672473]","DOI":"10.1101\/2025.07.09.25331239"},{"key":"R105","doi-asserted-by":"publisher","DOI":"10.3389\/fdgth.2025.1616488","article-title":"Generative AI in consumer health: leveraging large language models for health literacy and clinical safety with a digital health framework","volume":"7","author":"Tilton","journal-title":"Front Digit Health"},{"issue":"1","key":"R106","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-09138-0","article-title":"The need for guardrails with large language models in pharmacovigilance and other medical safety critical settings","volume":"15","author":"Hakim","journal-title":"Sci Rep"},{"issue":"1","key":"R107","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01258-7","article-title":"A framework for human evaluation of large language models in healthcare derived from literature review","volume":"7","author":"Tam","journal-title":"NPJ Digit Med"}],"container-title":["Journal of Medical Internet Research"],"original-title":[],"language":"en","deposited":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T20:51:34Z","timestamp":1786481494000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jmir.org\/2026\/1\/e90744"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,11]]},"references-count":107,"URL":"https:\/\/doi.org\/10.2196\/90744","relation":{},"ISSN":["1438-8871"],"issn-type":[{"value":"1438-8871","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,11]]},"article-number":"v28i1e90744"}}