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Related Experiment Video

Updated: May 31, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

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

David A Cook1,2, Joshua Overgaard1, V Shane Pankratz3

  • 1Division of General Internal Medicine, Mayo Clinic College of Medicine and Science, Rochester, MN, United States.

Journal of Medical Internet Research
|January 24, 2025
PubMed
Summary

Large language model (LLM)-powered virtual patients (VPs) can create realistic dialogues and provide personalized feedback for medical training. This scalable, cost-effective approach enhances clinical performance assessment.

Keywords:
clinical decision-makingclinical reasoningcomputer-assisted instructionmachine learningnatural language generationnatural language processingsimulation trainingvirtual patient

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Area of Science:

  • Medical Education Technology
  • Artificial Intelligence in Healthcare
  • Clinical Simulation

Background:

  • Virtual patients (VPs) are computer-based simulations of patient-clinician interactions.
  • Current VP use is constrained by high costs and limited scalability.

Purpose of the Study:

  • To evaluate large language models (LLMs) for generating authentic virtual patient dialogues.
  • To assess LLM capabilities in accurately representing patient preferences and delivering personalized feedback.
  • To explore LLMs' utility in rating the quality of simulated dialogues and feedback.

Main Methods:

  • Intrinsic evaluation of 60 virtual patient-clinician conversations using engineered prompts for OpenAI's GPT.
  • Two outpatient medicine topics (chronic cough, diabetes management) with varied patient preferences.
  • Dialogue authenticity and feedback quality rated by physicians and GPT; user experience and bias assessed.

Main Results:

  • LLM-powered VP conversations cost significantly less than traditional methods (US $0.02-$0.51 per conversation).
  • High ratings for dialogue authenticity (4.7/6), user experience (4.9/6), and feedback quality (4.7/6).
  • LLM-generated feedback ratings comparable to human ratings; dialogue authenticity was lower in self-chat scenarios.

Conclusions:

  • LLM-powered virtual patients offer a scalable, accessible, and cost-effective solution for medical training.
  • These systems effectively simulate patient-clinician dialogues and provide personalized performance feedback.
  • LLM-generated ratings for feedback quality demonstrate parity with human expert evaluations.