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Developing AI-powered virtual patient chatbots for diagnostic reasoning training.

Yejin Han¹, Yuyi Park², Jihyun Lee²

¹Department of Medical Education & Humanities, College of Medicine, Yeungnam University, Daegu, Republic of Korea.

Academic Medicine : Journal of the Association of American Medical Colleges

|March 26, 2026

Summary

AI-powered virtual patient chatbots offer an effective and scalable solution for medical education, enhancing diagnostic reasoning skills through simulated clinical encounters. These tools provide valuable practice and feedback, improving diagnostic accuracy and clinical competency.

Keywords:

artificial intelligence (AI)chatbot clinical training diagnostic reasoning virtual patient

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Area of Science:

Medical Education Technology
Artificial Intelligence in Healthcare
Cognitive Science in Medicine

Background:

Traditional medical education relies on resource-intensive methods like standardized patients for teaching diagnostic reasoning.
Limitations of current methods include significant time, space, and cost requirements.

Purpose of the Study:

To develop and evaluate AI-powered virtual patient chatbots for practicing diagnostic reasoning in medical education.
To address the resource limitations of traditional simulation methods.

Main Methods:

AI-powered virtual patient chatbots were developed using natural language processing, guided by dual process and memory models of diagnostic reasoning.
Chatbots simulated physician-patient interactions across three clinical scenarios, allowing students to practice differential diagnoses and receive immediate feedback.

Main Results:

Chatbots were rated highly for effectiveness (4.50/5) and usability (4.46/5) by students and faculty.
Participants found chatbots supported key diagnostic reasoning processes, including hypothetico-deductive reasoning and pattern recognition.
Faculty noted cost-efficiency and scalability; students valued authentic reasoning practice and reflective learning opportunities.

Conclusions:

AI-powered virtual patient chatbots provide a feasible and educationally valuable environment for diagnostic reasoning practice.
Future refinements should focus on case complexity, patient-centered language, and equity.
Further research is needed to evaluate educational impact and guide integration into clinical training.