This paper is in the following e-collection/theme issue:

Chatbots and Conversational Agents (740) Virtual Patients (77) Cost-Effectiveness and Economics (355) Natural Language Processing (917) Clinical Information and Decision Making (1915) Artificial Intelligence (AI) in Medical Education (182)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/68486, first published .
Virtual Patients Using Large Language Models: Scalable, Contextualized Simulation of Clinician-Patient Dialogue With Feedback

Virtual Patients Using Large Language Models: Scalable, Contextualized Simulation of Clinician-Patient Dialogue With Feedback

Virtual Patients Using Large Language Models: Scalable, Contextualized Simulation of Clinician-Patient Dialogue With Feedback

Authors of this article:

David A Cook1, 2 Author Orcid Image ;   Joshua Overgaard1 Author Orcid Image ;   V Shane Pankratz3 Author Orcid Image ;   Guilherme Del Fiol4 Author Orcid Image ;   Chris A Aakre1 Author Orcid Image
Article Authors Cited by (17) Tweetations (4) Metrics

Journals

  1. Haider S, Prabha S, Gomez-Cabello C, Borna S, Genovese A, Trabilsy M, Collaco B, Wood N, Bagaria S, Tao C, Forte A. Synthetic Patient–Physician Conversations Simulated by Large Language Models: A Multi-Dimensional Evaluation. Sensors 2025;25(14):4305 View
  2. Luo M, Bi S, Pang J, Liu L, Tsui C, Lai Y, Chen W, Yang Y, Xu K, Zhao L, Jin L, Lin D, Wu X, Chen J, Chen R, Liu Z, Zou Y, Yang Y, Li Y, Lin H. A large language model digital patient system enhances ophthalmology history taking skills. npj Digital Medicine 2025;8(1) View
  3. Jadoon M, Naushad K, Aman F, Khan Y, Durrani M, Ali S. Virtual Patients for Communication Skills Training: A Mixed Methods Evaluation. Cureus 2025 View
  4. Liu Y, Shi C, Wu L, Lin X, Chen X, Zhu Y, Tan H, Zhang W. Development and Validation of a Large Language Model–Based System for Medical History-Taking Training: Prospective Multicase Study on Evaluation Stability, Human-AI Consistency, and Transparency. JMIR Medical Education 2025;11:e73419 View
  5. Gin B, LaForge K, Burk-Rafel J, Boscardin C. Macy Foundation Innovation Report Part II: From Hype to Reality: Innovators’ Visions for Navigating AI Integration Challenges in Medical Education. Academic Medicine 2025;100(9S):S22 View
  6. Wang Q, Chen Z, Zhang H, Zhou Y, Du C, Hu W, Lv X, Xie T, Zheng H. Large language models could be applied in personalized out-of-hospital management for breast cancer: a prospective randomized single blind study. Scientific Reports 2025;15(1) View
  7. Xin W, Yi H, Song J, Tian Z, Chen H, Tan S. Can AI replace clinician-rated depression scales? The psychometric properties of HAMLET – Hamilton Large-Language-Model Evaluation Tool. Asian Journal of Psychiatry 2025;113:104707 View
  8. Haimovich A, Janke A, Kocher K, Mangus C, Parsons A, McCoy L, Taylor R, Rodman A, Pusic M. Managing Clinical Uncertainty: Formalizing Management Reasoning in Emergency Care Delivery. Annals of Emergency Medicine 2025 View
  9. Sheth U, Lo M, McCarthy J, Baath N, Last N, Guo E, Monteiro S, Sibbald M. Understanding the Role of Large Language Model Virtual Patients in Developing Communication and Clinical Skills in Undergraduate Medical Education. International Medical Education 2025;4(4):39 View
  10. Li D, Lebai Lutfi S. LLM-based Virtual Patient Systems for History-Taking in Medical Education: A Comprehensive Systematic Review (Preprint). JMIR Medical Informatics 2025 View
  11. Jacobs C, Johnson H, Tan N, Brownlie K, Joiner R, Thompson T. Application of AI Communication Training Tools in Medical Undergraduate Education: Mixed Methods Feasibility Study Within a Primary Care Context. JMIR Medical Education 2025;11:e70766 View
  12. Dymm B, Khalid A. Artificial Intelligence in Neurology and Stroke Education: Current Applications and Future Directions. Seminars in Neurology 2025 View
  13. Morjaria L, Gandhi B, Burns L, Sibbald M. Generative AI: A Disruptor to Health Professions Learner Assessment. Cureus 2025 View
  14. Kewalramani D, Jawa R, Martin C, Gosain A, Wan D, Varghese T, Boland G, Dream S, Sifri Z, Mollen K, Chu D, Narayan M. Position statement from the society of University surgeons, surgical education committee: Artificial intelligence in surgical training for medical students, residents, and fellows. Surgery 2026;190:109849 View
  15. Jo Y, Lee M, Yang H. Large Language Model-Based Virtual Patient Simulations in Medical and Nursing Education: A Review. Applied Sciences 2025;15(22):11917 View
  16. Zeng J, Qi W, Shen S, Liu X, Li S, Wang B, Dong C, Zhu X, Shi Y, Lou X, Wang B, Yao J, Jiang G, Zhang Q, Cao S. Embracing the Future of Medical Education With Large Language Model–Based Virtual Patients: Scoping Review. Journal of Medical Internet Research 2025;27:e79091 View

Conference Proceedings

  1. Al Owayyed M, Denga A, Brinkman W. Proceedings of the 25th ACM International Conference on Intelligent Virtual Agents. Controlled Yet Natural: A Hybrid BDI-LLM Conversational Agent for Child Helpline Training View