An ML-based decision support tool can effectively predict residency interview offers and help mitigate biases in the applicant screening process.
May support bias mitigation in residency screening; leaves open prospective validation before practice adoption.
The authors developed and validated an ML algorithm for predicting residency interview offers from numerous application elements with high performance-even when USMLE scores were removed. Model deployment in a DST highlighted its potential for screening candidates and helped quantify and mitigate biases existing in the selection process. Further work will incorporate unstructured textual data through natural language processing methods.
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Burk‐Rafel et al. (2021) studied this question.
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