AI in Medical School Admissions: Revolutionizing or Maintaining the Status Quo?
In my nearly 25-year career in medicine, medical education, and medical school admissions, I’ve always believed that bias is an inherent part of the admissions process. To combat such biases, medical schools started employing holistic admissions years ago and, in 2011, the Association of American Medical Colleges established the 15 core competencies they want to see in medical school applicants. However, even with these initiatives and required bias training for medical school faculty, I believe it is impossible to completely erase subjectivity in the human decision-making process when evaluating medical school applications.
New York University School of Medicine has developed a machine learning algorithm using several years of admissions data to create a ‘virtual faculty screener’ that is now used for the initial review of medical school applicants. This screener helps decide if the applicant will be rejected, interviewed, or held for further review. When developing the algorithm, NYU researchers found that the AI results mirrored the screening decisions made by individual faculty members, indicating a high level of accuracy.
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A limitation of both the Zucker and NYU models is their exclusion of more ‘subjective’ written components of the application, such as activity descriptions, the medical school personal statement, secondary essays, and letters of recommendation. However, Zucker notes that ‘there has been a recent movement to reduce the emphasis on personal statements and medical school letters of recommendation, which both have limited value in differentiating applicants or predicting medical school performance.’ This raises questions about the balance between objective and subjective criteria in admissions.
In 2021, the American Association of Colleges of Osteopathic Medicine (AACOM) began developing a machine learning program to evaluate medical school applications. Currently in development and being trained on previous admissions data including the personal statement and activities entries, the program aims to screen applicants when deciding whom to interview, assess an applicant’s fit for a given medical school, increase equity in the admissions process, and predict the likelihood of matriculation.
About Jessica Freedman, M.D.
Jessica Freedman, M.D. is the founder of MedEdits Medical Admissions, which provides personalized guidance to premeds, medical school applicants, and residency applicants. She is an emergency physician and former faculty member at the Icahn School of Medicine at Mount Sinai where she served on medical school and residency admissions committees, and worked in curriculum design and medical education. She believes that every applicant pursuing a career in medicine should have access to honest and accurate information.
