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Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/66905, first published .
Using Large Language Models for Chronic Disease Management Tasks: Scoping Review

Using Large Language Models for Chronic Disease Management Tasks: Scoping Review

Using Large Language Models for Chronic Disease Management Tasks: Scoping Review

Journals

  1. Zhang B, Lau L, Chen Y. Trends in digital mental health interventions: A 20-year bibliometric analysis. DIGITAL HEALTH 2025;11 View
  2. Khosravi M, Zamaninasab Z, Mojtabaeian S, Dindar Demiray E, Arab-Zozani M, Shi M. A systematic review of the limitations of large language models in generating healthcare content. PLOS Digital Health 2026;5(4):e0001354 View
  3. Huang Q, Zhu W, Mo H, Huang B, Liao Z, Lu X, Zhang H. Comparative evaluation of ChatGPT-5.0, DeepSeek-R1, and Gemini-2.5 pro in real-world outpatient prescription counseling: A multidimensional analysis. DIGITAL HEALTH 2026;12 View
  4. Jefferson T, Phillips-Wren G. Leveraging generative AI for post-disaster resource decisions in emergency management: a case study of Hurricane Helene and the medicated society. Journal of Decision Systems 2026;35(1) View
  5. Han G, Lee H, Son Y. Natural language processing-based chatbots for chronic disease self-management: A systematic review of implementation and health outcomes. DIGITAL HEALTH 2026;12 View

Conference Proceedings

  1. Serugunda H, Jianquan O, Katende J. 2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). A Context Adaptive Instruction Tuning Framework for Diverse Clinical NLP Tasks View