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Comparing faculty and artificial intelligence in grading ophthalmology residency applications.

Jared Moon1, Owen Sorensen2, Priyam Mazumdar3

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Academic Medicine : Journal of the Association of American Medical Colleges
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Artificial intelligence (AI) can predict ophthalmology residency match outcomes, offering an efficient and objective tool to assist faculty reviewers. This AI application helps reduce administrative workload and potential human bias in the competitive residency selection process.

Keywords:
algorithmartificial intelligenceophthalmologyresidency matchresident selection

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Area of Science:

  • Medical Education
  • Artificial Intelligence in Medicine
  • Ophthalmology

Background:

  • The volume of residency applications and data per applicant are increasing, emphasizing holistic review and leading to application inflation.
  • Artificial intelligence (AI) has shown potential to augment human review in resident selection, identifying successful candidates who might otherwise be overlooked.

Purpose of the Study:

  • To determine if AI can accurately predict match outcomes for ophthalmology residents.
  • To assess AI's validity as a tool for improving the objectivity and efficiency of the residency match process.

Main Methods:

  • A prospective study of 513 U.S. medical doctor graduates from the 2023-2024 San Francisco Match cycle.
  • AI (GPT 3.5 Turbo) and faculty prospectively analyzed complete application data using different methods (AI with 5 criteria, faculty with a standardized rubric).
  • The primary outcome was the predictiveness of each rank list on the applicant's match outcome.

Main Results:

  • Both AI and faculty rank lists were significantly predictive of matching to an ophthalmology residency (P < .001).
  • Each 10-percentile increase in AI ranking correlated with a 23% increase in the odds of a match (OR=1.23).
  • Each 10-percentile increase in faculty ranking correlated with a 41% increase in the odds of a match (OR=1.41).

Conclusions:

  • AI accurately predicts ophthalmology residency match outcomes.
  • AI can serve as an adjunct to faculty review, reducing administrative workload and human bias.
  • AI offers a scalable method to support residency application review while preserving faculty oversight.