Use of a Machine Learning Program for Urogynecology Fellowship Applicant Review

IMPORTANCE: There is a gap in objective methods to review applications for advanced medical training. OBJECTIVE: The objective of this study was to evaluate the accuracy of a new machine learning-based residency and fellowship applicant review program, Halsted (Medicratic) in urogynecology fellowship applicant selection compared with program director (PD) review. STUDY DESIGN: This Institutional Review Board-approved study compared PD's standard assessment of fellowship applicants to the Halsted-based assessment at 3 programs in the 2023-2024 application cycle. Each program provided a score for each candidate on a 100-point scale in several domains. After the conclusion of the match, each PD completed a profile within Halsted that identified their preferred qualities in applicants. Halsted scores were obtained, which were compared with PD scores. RESULTS: A total of 126 applications were reviewed, with 59 applicants reviewed by more than 1 program. Program 1 ( r =0.60; P =0.0019) and Program 2 ( r =0.58; P