An ML-based decision support tool can effectively predict residency interview offers and help mitigate biases in the applicant screening process.
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.
Burk‐Rafel et al. (Wed,) studied this question.