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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Large Language Model-Based Patient Simulation to Foster Communication Skills in Health Care Professionals:

Ahmed Elhilali1, Andy Suy-Huor Ngo1, Daniel Reichenpfader1

  • 1Institute Patient-centered Digital Health, Bern University of Applied Sciences, Biel, Switzerland.

JMIR Medical Education
|December 12, 2025
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Summary
This summary is machine-generated.

Large language models (LLMs) offer a scalable solution for medical education patient simulations. This highly usable tool requires a feedback mechanism to maximize its potential for communication skills training.

Keywords:
chatbotlarge language modelmedical educationpatient simulationvignette

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Area of Science:

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

Background:

  • Standardized patients are crucial for medical communication skills training.
  • Actor-based simulations face limitations in sociodemographic diversity and scalability.
  • Large language models (LLMs) present a promising avenue for diverse and scalable patient simulations.

Purpose of the Study:

  • To introduce the system architecture of a digital tool using LLMs for medical history-taking simulations.
  • To assess the usability of the LLM-based patient simulation tool.
  • To explore differences among LLMs in simulating patient encounters.

Main Methods:

  • User-centered design process with input from medical students.
  • Development of a web prototype integrating 5 LLMs (OpenAI, Anthropic, xAI).
  • Usability testing with medical students (SUS questionnaire) and qualitative feedback.
  • Exploratory analysis of simulation quality by practicing physicians.

Main Results:

  • The tool demonstrated high perceived usability (mean SUS score 91.5).
  • Users desired a "didactic loop" with automated feedback for enhanced learning.
  • LLMs showed limitations in simulating uncertainty and conversational flow but excelled in symptom coherence and realistic timelines.
  • Differences between LLMs were not statistically significant, with limited discriminative reliability in evaluations.

Conclusions:

  • A highly usable patient simulation tool leveraging LLMs was successfully developed.
  • Integration of an automated feedback mechanism is crucial for realizing the tool's full potential in communication training.
  • Future research should focus on LLM simulation of psychosocial patient characteristics.