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A Language Model-Powered Simulated Patient With Automated Feedback for History Taking: Prospective Study.

Friederike Holderried1, Christian Stegemann-Philipps1, Anne Herrmann-Werner1

  • 1Tübingen Institute for Medical Education (TIME), Medical Faculty, University of Tübingen, Tübingen, Germany.

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Summary
This summary is machine-generated.

Generative Pretrained Transformer (GPT) 4 effectively provided structured feedback for medical students practicing history taking with simulated patients. This artificial intelligence tool shows promise for enhancing medical education, despite minor limitations in feedback specificity.

Keywords:
ChatGPTGPT: LLMLLMsNLPTELartificial intelligencechatbotchatbotscommunicationcommunication skillsconversational agentconversational agentshistorieshistoryinteractioninteractionslanguage modellanguage modelsmachine learningmedical educationnatural language processingrelationshiprelationshipssimulatedstudentstudentstechnology enhanced educationvirtual patients communication

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Area of Science:

  • Medical Education
  • Artificial Intelligence in Healthcare
  • Clinical Skills Training

Background:

  • History taking is crucial for medical diagnosis but challenging to teach and provide feedback on due to resource limitations.
  • Virtual simulated patients and AI-powered chatbots offer innovative solutions for medical training.
  • Large Language Models (LLMs) are advancing the realism and feedback capabilities of these educational tools.

Purpose of the Study:

  • To evaluate the effectiveness of a Generative Pretrained Transformer (GPT) 4 model in delivering structured feedback on medical students' history-taking skills.
  • To assess the quality and reliability of AI-generated feedback compared to human assessment.

Main Methods:

  • A prospective study was conducted with medical students interacting with a GPT-4 powered chatbot simulating patients.
  • The chatbot provided immediate feedback on the comprehensiveness of history-taking.
  • Student-chatbot interactions were analyzed, comparing GPT-4 feedback with human rater feedback for interrater reliability and feedback quality.

Main Results:

  • GPT-4's role-play and responses were medically plausible in over 99% of cases.
  • High interrater reliability (Cohen κ=0.832) was observed between GPT-4 and human raters.
  • Minor disagreements (κ<0.6) occurred in 8 categories, indicating potential issues with feedback specificity or divergence from human judgment.

Conclusions:

  • The GPT-4 model demonstrated effectiveness in providing structured feedback for medical students' history-taking dialogues.
  • While some limitations in feedback specificity were noted, the high agreement with human raters supports LLMs as valuable tools in medical education.
  • The study advocates for the careful integration of AI-driven feedback mechanisms in medical training, highlighting key considerations for LLM implementation.