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Artificial intelligence: Reducing inconsistency in the surgical residency application review process.

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Area of Science:

  • Medical Education
  • Artificial Intelligence in Healthcare
  • Surgical Residency Recruitment

Background:

  • Traditional residency recruitment faces challenges with subjective application reviews.
  • Medical Student Performance Evaluation (MSPE) letters are crucial but can be inconsistently evaluated.
  • Artificial intelligence (AI) offers potential for objective and consistent evaluation.

Purpose of the Study:

  • To assess the consistency of AI, specifically ChatGPT, in evaluating MSPE letters.
  • To compare AI's evaluation consistency against experienced human reviewers in surgical residency recruitment.
  • To identify AI's limitations and areas for refinement in the recruitment process.

Main Methods:

  • Utilized ChatGPT to evaluate MSPE letters for general surgery residency applicants.
  • Compared ChatGPT's grading consistency with that of experienced human reviewers.
  • Analyzed the performance evaluation data for discrepancies and consistencies.

Main Results:

  • ChatGPT demonstrated higher consistency in grading MSPE letters compared to human reviewers.
  • AI evaluation, while consistent, still presents limitations requiring careful implementation.
  • The study highlights AI's potential to improve objectivity in residency selection.

Conclusions:

  • AI, particularly ChatGPT, offers a more consistent approach to evaluating MSPE letters in residency recruitment.
  • Careful refinement and further research are essential for effective AI integration into surgical residency selection.
  • AI has the potential to enhance the efficiency and fairness of residency application reviews.