Using Machine Learning in Residency Applicant Screening

On this episode of the Academic Medicine Podcast, guest Jesse Burk-Rafel, MD, MRes, joins hosts Toni Gallo and Research in Medical Education (RIME) Committee member Mahan Kulasegaram, PhD, to discuss the development of a decision support tool that incorporates machine learning and the use of that tool in residency applicant screening. They also talk about the residency application process and potential ways that artificial or augmented intelligence (AI) might mitigate current challenges.

This episode is now available through Apple PodcastsSpotify, and wherever else you get your podcasts.

This is the first episode in a 3-part series of discussions with RIME authors about their medical education research and its implications for the field. Find the complete 2021 RIME supplement, which is free to read and download, at

Read the article discussed in this episode: Development and Validation of a Machine-Learning-Based Decision Support Tool for Residency Applicant Screening and Review. 

A transcript of this episode is available at

Further Reading

Burk-Rafel J, Reinstein I, Feng J, et al. Development and validation of a machine-learning-based decision support tool for residency applicant screening and review [published online ahead of print August 3, 2021]. Acad Med. doi: 10.1097/ACM.0000000000004317.