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A Competency Framework for Medical AI Education: Mixed Methods Study.

Chang Cai1,2, Gaoxia Zhu3, Shang-Ming Zhou4

  • 1Lee Kong Chian School of Medicine, Nanyang Technological University, 50 Nanyang Avenue, Singapore, Singapore, 65 82634539.

JMIR Medical Education
|May 20, 2026
PubMed
Summary

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This summary is machine-generated.

This study developed a medical artificial intelligence (AI) competency framework and training program to address clinician barriers in AI adoption. The pilot program showed feasibility and high participant satisfaction, suggesting a structured approach for AI education in healthcare.

Area of Science:

  • Medical Education
  • Artificial Intelligence
  • Health Informatics

Background:

  • Clinicians face challenges adopting artificial intelligence (AI) in healthcare due to a lack of understanding, trust, and interpretation difficulties.
  • Existing AI competency frameworks lack clinical specificity, and evidence for framework-based medical training is limited.

Purpose of the Study:

  • To develop a specialized medical artificial intelligence (AI) competency framework.
  • To design and pilot an AI training program based on the developed framework for medical professionals.

Main Methods:

  • A mixed-methods approach integrating the UNESCO AI framework with the Miller pyramid model to create the competency framework.
  • Expert input from 24 stakeholders and deductive content analysis were used for framework refinement.
Keywords:
AIAI competency frameworkAI literacyAI trainingUNESCOUnited Nations Educational, Scientific and Cultural Organizationartificial intelligencemedical educationmedical professionals

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  • A 2-round Delphi process with 9 educators and a pilot workshop with 28 participants evaluated the training program's feasibility.

    • Main Results:

    • A 6D 4-level medical AI competency framework was successfully developed, emphasizing AI foundations and application skills.
    • The framework informed a 5-module training program, achieving full consensus among educators via the Delphi process.
    • The pilot workshop demonstrated high participant satisfaction and engagement, with moderate confidence, indicating program feasibility.

    Conclusions:

    • The developed framework offers a structured reference for designing AI training in medical education.
    • Preliminary findings support the feasibility of the AI training module, but broader, long-term evaluations are necessary.
    • Future research should expand the framework and training program to diverse settings and assess long-term impact.