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Development and Validation of a Large Language Model-Based System for Medical History-Taking Training: Prospective

Yang Liu1, Chujun Shi1, Liping Wu1

  • 1Medical Simulation Center, Shantou University Medical College, No. 22 Xinling Road, Shantou, 515041, China, 86 754-88900459.

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Summary

The AI-Powered Medical History-Taking Training and Evaluation System (AMTES) offers consistent, transparent feedback for medical students. This AI-driven tool demonstrates high accuracy and stability, enhancing clinical skills in history-taking simulations.

Keywords:
DeepSeekQwencross-model generalizabilityevaluation stabilityevaluation transparencyhuman-AI consistencylarge language modelsmedical history-takingstructured evaluationvirtual standardized patient

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Area of Science:

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

Background:

  • Traditional medical history-taking training lacks consistent feedback, standardized evaluation, and access to standardized patients.
  • Artificial intelligence (AI)-powered simulated patients present a potential solution, but human-AI consistency and evaluation stability require further investigation.

Purpose of the Study:

  • To develop and validate the AI-Powered Medical History-Taking Training and Evaluation System (AMTES) using DeepSeek-V2.5.
  • To assess AMTES's stability, human-AI consistency, and transparency in diverse clinical scenarios.

Main Methods:

  • Developed AMTES with strategies for dialog quality and automated assessment.
  • Conducted a prospective study with 31 medical students evaluating 3 cases (simple, moderate, complex).
  • Performed systematic baseline comparisons and tested generalizability with an alternative large language model (LLM).

Main Results:

  • AMTES achieved high dialog accuracy (97.9%-99.0%) and contextual appropriateness (>99%).
  • Automated assessments showed exceptional stability (CVs ≤1.2%) and high human-AI consistency (ICCs >0.923).
  • 87% of students found AMTES helpful, with 83% willing to use it again.

Conclusions:

  • AMTES provides significant educational value through authentic clinical dialogs and consistent, transparent feedback.
  • The system is adaptable and generalizable for medical history-taking training across various educational settings.
  • Strong user approval supports AMTES's potential as a valuable training tool.