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Artificial Intelligence Methods and Artificial Intelligence-Enabled Metrics for Surgical Education: A

S Swaroop Vedula1, Ahmed Ghazi2, Justin W Collins3

  • 1From the Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD (Vedula).

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Summary

Artificial intelligence (AI) methods and metrics can significantly improve surgical education. A Delphi survey of 40 experts established a consensus roadmap for AI applications in surgical training, assessment, and feedback.

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Area of Science:

  • Surgical Education
  • Artificial Intelligence
  • Medical Technology

Background:

  • Artificial intelligence (AI) methods and AI-enabled metrics offer significant potential for advancing surgical education.
  • There is a need for consensus guidance on specific requirements for AI in this field.

Purpose of the Study:

  • To generate consensus guidance on the specific needs for AI methods and AI-enabled metrics in surgical education.
  • To outline a roadmap for the future application of AI in surgical training, assessment, and feedback.

Main Methods:

  • A systematic literature search, virtual conference, and accelerated 3-round Delphi survey involving 40 multidisciplinary stakeholders.
  • Consensus was defined as agreement among 80% or more respondents.
  • Survey responses were coded into 11 themes and descriptively analyzed.

Main Results:

  • Consensus was achieved on 136 out of 155 questions (87.7%).
  • The panel identified key deliverables for AI-enhanced learning curve analytics and surgical skill assessment.
  • Priority deliverables for feedback were identified across 2-, 5-, and 10-year timeframes, including immediate post-operative performance feedback and anatomical recognition in surgical images.

Conclusions:

  • The Delphi panel consensus provides a clear and forward-looking roadmap for AI methods and AI-enabled metrics in surgical education.
  • This guidance will shape the future development and implementation of AI tools for surgical training and assessment.