Nedos, I. et al. Is artificial intelligence ready for emergency department triage? a retrospective evaluation of multiple large language models in 39,375 patients at a university emergency department. J. Clin. Med. 15, 1512 (2026).
Gaber, F. et al. Evaluating large language model workflows in clinical decision support for triage and referral and diagnosis. NPJ Digit. Med. 8, 263 (2025).
Xie, Q. et al. Medical foundation large language models for comprehensive text analysis and beyond. NPJ Digit. Med. 8, 141 (2025).
Biswas, A. & Talukdar, W. Intelligent clinical documentation: harnessing generative AI for patient-centric clinical note generation. Int. J. Innov. Sci. Res. Technol. https://doi.org/10.38124/ijisrt/IJISRT24MAY1483 (2024).
Maity, S. & Saikia, M. J. Large language models in healthcare and medical applications: a review. Bioengineering 12, 631 (2025).
Aydin, S., Karabacak, M., Vlachos, V. & Margetis, K. Large language models in patient education: a scoping review of applications in medicine. Front. Med. 11, 1477898 (2024).
Lin, C. & Kuo, C. -F. Roles and potential of large language models in healthcare: a comprehensive review. Biomed. J. 48, 100868 (2025).
Jörke, M. et al. GPTCoach: towards LLM-based physical activity coaching. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems https://doi.org/10.1145/3706598.3713819 (2024).
Ong, Q. C. et al. Advancing health coaching: a comparative study of large language model and health coaches. Artif. Intell. Med. 157, 103004 (2024).
Schulman-Green, D. et al. Processes of self-management in chronic illness. J. Nurs. Scholarsh. 44, 136–144 (2012).
Peerbolte, T. F. et al. Conversational agents supporting self-management in people with a chronic disease: systematic review. J. Med. Int. Res. 27, e72309 (2025).
Serugunda, H. M. et al. Using large language models for chronic disease management tasks: scoping review. JMIR Med. Inform. 13, e66905 (2025).
Shayaninasab, M., Zahoor, M. & Yalçin, Ö. N. Enhancing patient intake process in mental health consultations using rag-driven chatbot. In 2024 12th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW), 256–264 https://doi.org/10.1109/ACIIW63320.2024.00053 (IEEE, 2024).
Ayers, J. W. et al. Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum. JAMA Int. Med. 183, 589–596 (2023).
Haag, D. et al. The last JITAI? exploring large language models for issuing just-in-time adaptive interventions: fostering physical activity in a prospective cardiac rehabilitation setting. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, 1–18 https://doi.org/10.1145/3706598.3713307 (2024).
Artsi, Y. et al. Large language models in real-world clinical workflows: a systematic review of applications and implementation. Front. Digit. Health 7, 1659134 (2025).
Farzan, M., Ebrahimi, H., Pourali, M. & Sabeti, F. Artificial intelligence-powered cognitive behavioural therapy chatbots, a systematic review. Iran. J. Psychiatry 20, 102–110 (2025).
Wang, J. et al. Psychological counseling cannot be achieved overnight: automated psychological counseling through multi-session conversations. Preprint at https://arxiv.org/abs/2506.06626 (2025).
McFadyen, J. et al. Increasing engagement with cognitive-behavioral therapy (CBT) using generative AI: a randomized controlled trial (RCT). Commun. Med. 6, 129 (2026).
Sinha, C., Thakkar, R., Meheli, S. & Dinesh, D. Exploring the role of app features in providing continuity of care to users on a digital mental health platform (Wysa): Retrospective mixed methods observational study. JMIR Form. Res. 10, e73033 (2026).
Zhang, C. et al. A survey on multi-turn interaction capabilities of large language models. Preprint at https://arxiv.org/abs/2501.09959 (2025).
Uijen, A. A., Schers, H. J., Schellevis, F. G. & van den Bosch, W. J. How unique is continuity of care? a review of continuity and related concepts. Fam. Pract. 29, 264–271 (2012).
Saultz, J. W. & Lochner, J. Interpersonal continuity of care and care outcomes: a critical review. Ann. Fam. Med. 3, 159–166 (2005).
Gray, D. J. P., Sidaway-Lee, K., White, E., Thorne, A. & Evans, P. H. Continuity of care with doctors—a matter of life and death? A systematic review of continuity of care and mortality. BMJ Open 8, e021161 (2018).
Van Walraven, C., Oake, N., Jennings, A. & Forster, A. J. The association between continuity of care and outcomes: a systematic and critical review. J. Eval. Clin. Practice 16, 947–956 (2010).
Zhang, T. et al. History-aware hierarchical transformer for multi-session open-domain dialogue system. In Findings of the Association for Computational Linguistics: EMNLP 2022, 3395–3407 https://doi.org/10.18653/v1/2022.findings-emnlp.247 (2022).
Maharana, A. et al. Evaluating very long-term conversational memory of LLM agents. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 13851–13870 https://doi.org/10.18653/v1/2024.acl-long.747 (2024).
Ge, Y. et al. TReMu: towards neuro-symbolic temporal reasoning for LLM-agents with memory in multi-session dialogues. In Findings of the Association for Computational Linguistics: ACL 2025, 18974–18988 https://doi.org/10.18653/v1/2025.findings-acl.972 (2025).
Reynolds, R. et al. A systematic review of chronic disease management interventions in primary care. BMC Fam. Pract. 19, 11 (2018).
Jones, D., Dunn, L., Watt, I. & Macleod, U. Safety netting for primary care: evidence from a literature review. Br. J. Gen. Pract. 69, e70–e79 (2019).
Callen, J. L., Westbrook, J. I., Georgiou, A. & Li, J. Failure to follow-up test results for ambulatory patients: a systematic review. J. Gen. Intern. Med. 27, 1334–1348 (2011).
Rothman, A. A. & Wagner, E. H. Chronic illness management: what is the role of primary care?. Ann. Intern. Med. 138, 256–261 (2003).
Almond, S., Mant, D. & Thompson, M. Diagnostic safety-netting. Br. J. Gen. Pract. 59, 872–874 (2009).
Li, I., Dey, A. & Forlizzi, J. A stage-based model of personal informatics systems. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’10), 557–566 https://doi.org/10.1145/1753326.1753409 (2010).
Nahum-Shani, I., Hekler, E. B. & Spruijt-Metz, D. Building health behavior models to guide the development of just-in-time adaptive interventions: a pragmatic framework. Health Psychol. 34, 1209–1219 (2015).
Hsu, T. -C. C. et al. Personalized interventions for behaviour change: a scoping review of just-in-time adaptive interventions. Br. J. Health Psychol. 30, e12766 (2024).
Bosschaerts, K. et al. Designing a just-in-time adaptive intervention with trigger detection and a generative chatbot: smoking cessation use case. Digit. Health https://doi.org/10.1177/20552076251381747 (2025).
Lu, T., Lin, Q., Yu, B. & Hu, J. A systematic review of strategies in digital technologies for motivating adherence to chronic illness self-care. NPJ Health Syst. 2, 13 (2025).
Chen, C. et al. Followupbot: an LLM-based conversational robot for automatic postoperative follow-up. In International Conference on Behavioural and Social Computing 252–260 (Springer Nature Singapore, 2025).
Mamykina, L., Smaldone, A. M. & Bakken, S. R. Adopting the sensemaking perspective for chronic disease self-management. J. Biomed. Inform. 56, 406–417 (2015).
Lin, G., Le, M. N., Truong, K. N. & Mariakakis, A. The cognitive strategies behind multimodal health sensemaking: a menstrual health tracking case study. in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies https://doi.org/10.1145/3749482 (2025).
Mulani, J. et al. Deep reinforcement learning based personalized health recommendations. In Deep Learning Techniques for Biomedical and Health Informatics, 231–255 (Springer, 2019).
Abbasian, M., Azimi, I., Rahmani, A. M. & Jain, R. Conversational health agents: a personalized large language model-powered agent framework. JAMIA Open 8, ooaf067 (2025).
Su, J. et al. Investigating the factors influencing users’ adoption of artificial intelligence health assistants based on an extended UTAUT model. Sci. Rep. 15, 18215 (2025).
Afroogh, S., Akbari, A., Malone, E., Kargar, M. & Alambeigi, H. Trust in AI: progress, challenges, and future directions. Humanit. Soc. Sci. Commun. 11, 1568 (2024).
Sivaraman, V., Bukowski, L. A., Levin, J., Kahn, J. M. & Perer, A. Ignore, trust, or negotiate: understanding clinician acceptance of AI-based treatment recommendations in health care. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 1–18 (2023).
Mick, I., Freger, S. M., van Keizerswaard, J., Gholiof, M. & Leonardi, M. Comprehensive endometriosis care: a modern multimodal approach for the treatment of pelvic pain and endometriosis. Ther. Adv. Reprod. Health 18, 26334941241277759 (2024).
Becker, C. M., Gattrell, W. T., Gude, K. & Singh, S. S. Reevaluating response and failure of medical treatment of endometriosis: a systematic review. Fertil. Steril. 108, 125–136 (2017).
Devan, H., Hale, L., Hempel, D., Saipe, B. & Perry, M. A. What works and does not work in a self-management intervention for people with chronic pain? Qualitative systematic review and meta-synthesis. Phys. Ther. 98, 381–397 (2018).
Edgley, K., Horne, A. W., Saunders, P. T. K. & Tsanas, A. Symptom tracking in endometriosis using digital technologies: knowns, unknowns, and future prospects. Cell Rep. Med. 4, 101192 (2023).
Trepanier, L. C. M. et al. Smartphone apps for menstrual pain and symptom management: a scoping review. Internet Interv. 31, 100605 (2023).
Requadt, E., Nahlik, A. J., Jacobsen, A. & Ross, W. T. Patient experiences of endometriosis diagnosis: a mixed methods approach. BJOG 131, 941–951 (2024).
Gracia, E. et al. The vulnerable phase of heart failure. Am. J. Ther. 25, e456–e464 (2018).
Greene, S. J. et al. The vulnerable phase after hospitalization for heart failure. Nat. Rev. Cardiol. 12, 220–229 (2015).
Regalbuto, R., Maurer, M. S., Chapel, D., Mendez, J. & Shaffer, J. A. Joint commission requirements for discharge instructions in patients with heart failure: is understanding important for preventing readmissions?. J. Card. Fail. 20, 641–649 (2014).
Heidenreich, P. A. et al. 2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure: a report of the American College Of Cardiology/American Heart Association Joint Committee on clinical practice guidelines. Circulation 145, e895–e1032 (2022).
Weiss, A. J. & Jiang, H. J. Overview of clinical conditions with frequent and costly hospital readmissions by payer, 2018. in Healthcare Cost and Utilization Project (HCUP) Statistical Brief #278 (Agency for Healthcare Research and Quality, 2021).
Lee, K. K., Yang, J., Hernandez, A. F., Steimle, A. E. & Go, A. S. Post-discharge follow-up characteristics associated with 30-day readmission after heart failure hospitalization. Med. Care 54, 365–372 (2016).
Tung, Y. -C., Chang, G. -M., Chang, H. -Y. & Yu, T. -H. Relationship between early physician follow-up and 30-day readmission after acute myocardial infarction and heart failure. PLoS ONE 12, e0170061 (2017).
Lainscak, M. et al. Self-care management of heart failure: practical recommendations from the Patient Care Committee of the Heart Failure Association of the European Society of Cardiology. Eur. J. Heart Fail. 13, 115–126 (2011).
Balaskas, A., Schueller, S. M., Cox, A. L. & Doherty, G. Ecological momentary interventions for mental health: a scoping review. PLoS ONE 16, e0248152 (2021).
Torous, J. et al. The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality. World Psychiatry 20, 318–335 (2021).
Haaker, J. et al. Deficient inhibitory processing in trait anxiety: Evidence from context-dependent fear learning, extinction recall and renewal. Biol. Psychol. 111, 65–72 (2015).
Hindmarch, T., Hotopf, M. & Owen, G. S. Depression and decision-making capacity for treatment or research: a systematic review. BMC Med. Ethics 14, 54 (2013).
Si, Y. et al. Quality, safety and disparity of an AI chatbot in managing chronic diseases: simulated patient experiments. NPJ Digit. Med. 8, 574 (2025).
Yu, C. et al. From passive to proactive: a multi-agent system with dynamic task orchestration for intelligent medical pre-consultation. Preprint at https://arxiv.org/abs/2511.01445 (2025).
Wu, D. et al. LongMemEval: benchmarking chat assistants on long-term interactive memory. In Proceedings of the International Conference on Learning Representations (2025).
Noah, B. et al. Impact of remote patient monitoring on clinical outcomes: an updated meta-analysis of randomized controlled trials. NPJ Digit. Med. 1, 20172 (2018).
Hamine, S., Gerth-Guyette, E., Faulx, D., Green, B. B. & Ginsburg, A. S. Impact of mHealth chronic disease management on treatment adherence and patient outcomes: a systematic review. J. Med. Internet Res. 17, e52 (2015).
Vegesna, A., Tran, M., Angelaccio, M. & Arcona, S. Remote patient monitoring via non-invasive digital technologies: a systematic review. Telemed. J. E Health 23, 3–17 (2017).
Smedslund, G., Osteras, N. & Hestevik, C. H. Effects of remote patient monitoring on health care utilization in patients with noncommunicable diseases: systematic review and meta-analysis. JMIR Mhealth Uhealth 13, e68464 (2025).
Merrill, M. A. et al. Transforming wearable data into personal health insights using large language model agents. Nat. Commun. 17, 1143 (2026).
Mamykina, L. et al. Personal discovery in diabetes self-management: discovering cause and effect using self-monitoring data. J. Biomed. Inform. 76, 1–8 (2017).
