Organizations create AI to diversify medical school admissions

A new predictive analytics model could help medical schools increase the socio-economic diversity of students in their MD programs, Tiber Health, a global network of medical universities, found.

The model is the first of its kind in the world, according to an Oct. 1 news release from Tiber Health. It is designed to allow admissions committees to rely less on the MCAT and more on performance data when evaluating applicants.

"It confirms our hypothesis that underrepresented students from lower economic backgrounds with below 500 MCAT scores can be successful in med school and pass the Step 1," Michael Mayrath, PhD, president of Tiber Health, said in the release. "MD programs that have used a traditional MCAT-reliant evaluation methodology can enhance or upgrade their admissions policies by implementing this data-driven, performance-based process. It's a more accurate measure of the capabilities of minority MD applicants from lower economic backgrounds who might not fit the traditional profile but possess the proficiencies and ambition to become the future physicians who are needed for an ever-changing U.S. population."

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