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Area of Science:

  • Medical education
  • Artificial intelligence in medicine
  • Surgical residency selection

Background:

  • Integrated plastic surgery residency programs face increasing application numbers.
  • Faculty review workload is growing due to more applications per position.
  • Artificial intelligence (AI) offers a solution for efficient and holistic application review.

Purpose of the Study:

  • To develop and validate a machine learning (ML) tool for screening residency applications.
  • To identify candidates likely to receive interview invitations using ML.

Main Methods:

  • Retrospective collection of applications from an integrated plastic surgery residency program (2022-2025).
  • Processing application data through four ML models: XGBoost, Random Forest, CatBoost, and LightGBM.
  • Training and validation on 2022-2024 data, with testing on 2025 data; performance assessed against faculty interview decisions.

Main Results:

  • The CatBoost algorithm demonstrated top performance with an AUROC of 0.92 and AUPRC of 0.668.
  • Sensitivity reached 95% and specificity 67% at an F1 score-maximizing threshold.
  • The total number of publications was the most significant feature influencing interview invitation decisions.

Conclusions:

  • A validated ML algorithm can accurately assist in selecting residency interviewees.
  • This AI tool can enhance the efficiency and holism of residency application reviews.