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Artificial Intelligence Screening of Medical School Applications: Development and Validation of a Machine-Learning

Marc M Triola1, Ilan Reinstein2, Marina Marin3

  • 1M.M. Triola is associate dean of educational informatics and director, Institute for Innovations in Medical Education, NYU Grossman School of Medicine, New York, New York; ORCID: https://orcid.org/0000-0002-6303-3112 .

Academic Medicine : Journal of the Association of American Medical Colleges
|March 8, 2023
PubMed
Summary
This summary is machine-generated.

A machine-learning algorithm accurately screened medical school applications, matching faculty performance. This AI tool shows promise for consistent and reliable admissions reviews, benefiting diverse applicant groups.

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Area of Science:

  • Medical education technology
  • Artificial intelligence in admissions
  • Healthcare application screening

Background:

  • Medical school admissions involve rigorous faculty screening.
  • Ensuring consistency and reliability in this process is crucial.
  • Exploring AI for initial application review can optimize efficiency.

Purpose of the Study:

  • To determine if a machine-learning algorithm can accurately screen medical school applications.
  • To assess the algorithm's performance against human faculty reviewers.
  • To evaluate the algorithm's impact on fairness for diverse applicant groups.

Main Methods:

  • Developed a virtual faculty screener algorithm using historical application data (2013-2017).
  • Validated the algorithm retrospectively and prospectively on thousands of applications.
  • Conducted a randomized trial comparing algorithm-driven vs. faculty-driven application reviews in 2019.

Main Results:

  • The algorithm demonstrated strong performance in retrospective and prospective validations (AUROC up to 0.83).
  • A randomized trial showed no significant differences in interview recommendation rates between the algorithm and faculty.
  • The algorithm performed equitably across female and underrepresented in medicine applicants.

Conclusions:

  • A virtual faculty screener algorithm effectively replicates human faculty screening of medical school applications.
  • This AI tool has the potential to enhance consistency and reliability in admissions.
  • The algorithm offers a promising solution for optimizing the medical school application review process.