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AI Competency: Current State and Challenges.

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  • 1Department of Family Practice, University of British Columbia , David Strangway Bldg, 5950 University Blvd 3rd Floor, Vancouver, BC, V6T 1Z3, Canada, + 1 604-822-5431.

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Summary
This summary is machine-generated.

As artificial intelligence (AI) advances, medical education needs new competencies focused on selecting appropriate AI tools, not just optimizing their use. This research proposes a framework to guide AI competency development for physicians.

Keywords:
AIartificial intelligencemedical education

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Area of Science:

  • Medical Education
  • Artificial Intelligence
  • Health Informatics

Background:

  • The rapid development of artificial intelligence (AI) necessitates defining new competencies for medical professionals.
  • Current focus is on optimizing AI output and understanding technical/normative considerations, but this may yield diminishing returns.
  • A shift towards competency in selecting appropriate AI tools is emerging as crucial.

Purpose of the Study:

  • To summarize current AI competency domains in medical education.
  • To introduce a hierarchical framework for AI competency development.
  • To identify challenges in developing and measuring AI competencies.

Main Methods:

  • Review of existing AI competency domains in medical education.
  • Proposal of a hierarchical framework: cognitive (understanding), operational (using), and meta-AI (choosing) domains.
  • Discussion of challenges in competency-based medical education and AI performance indicator development.

Main Results:

  • Current AI competency development needs further synthesis.
  • A proposed hierarchical framework includes cognitive, operational, and meta-AI competencies.
  • Challenges include defining meaningful competencies, context-specific measurement, and developing performance indicators.

Conclusions:

  • A new competency focused on choosing AI tools is essential for medical education.
  • The proposed framework can guide priority-setting for AI competency development.
  • Addressing challenges in measurement and indicator development is critical for successful AI integration in healthcare.