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The physical assessment examines the patient for objective data that defines the patient's condition, and aids in formulating the nursing care plan. The purpose of physical assessment is a health status appraisal, which includes identifying health problems, and establishing a database for nursing intervention.
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Related Experiment Video

Updated: May 5, 2026

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder
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AI-Driven Objective Structured Clinical Examination Generation in Digital Health Education: Comparative Analysis of

Zineb Zouakia1,2, Emmanuel Logak1,2, Alan Szymczak1,2

  • 1Clinical Bioinformatics Laboratory, Imagine Institute, Université Paris Cité, INSERM UMR1163, Paris, France.

JMIR Medical Education
|January 15, 2026
PubMed
Summary

Simulated-agents GPT significantly improved Objective Structured Clinical Examinations (OSCE) generation in digital health. This AI approach enhances content quality and usability, supporting artificial intelligence in medical education.

Keywords:
ChatGPTGPT-4odigital healthdigital health educationgenerative artificial intelligencelarge language modelsmedical educationmedical informaticsobjective structured clinical examinationprompt design

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Area of Science:

  • Medical Education Technology
  • Artificial Intelligence in Healthcare
  • Digital Health Training

Background:

  • Objective Structured Clinical Examinations (OSCEs) are crucial for medical education but resource-intensive, especially in digital health.
  • Large language models (LLMs) show promise for automating educational content creation, yet their use in generating OSCEs is under-researched.

Purpose of the Study:

  • To evaluate three GPT-4o configurations for generating digital health OSCE stations.
  • To compare standard GPT, personalized GPT (with reference book), and simulated-agents GPT (with structured prompts and reference book) for OSCE generation.

Main Methods:

  • Generated 24 OSCE stations across 8 digital health topics using three GPT-4o configurations.
  • Assessed format compliance by one expert and educational content by two blinded digital health experts using a detailed assessment grid.
  • Employed Kruskal-Wallis tests for statistical analysis of the results.

Main Results:

  • Simulated-agents GPT demonstrated superior format compliance and content quality, including accuracy (4.47/5) and clarity (4.46/5).
  • This configuration achieved 88% usability without major revisions and was preferred by experts.
  • Standard GPT scored lowest in clarity and educational value, while personalized GPT had the lowest format compliance.

Conclusions:

  • Structured prompting, especially simulating specialized agents, significantly enhances LLM-generated OSCE content reliability and usability.
  • AI, particularly advanced prompting techniques, can effectively support medical education content generation.
  • Expert validation remains essential for AI-generated educational materials.