AI in Medical School Admissions: A Game Changer for Future Physicians
When aspiring medical professionals throw their hats in the ring at the Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, they hope to dazzle with their achievements and aspirations. But here’s a twist: the very first reader of these hopeful applications isn’t a seasoned admissions officer—it’s an artificial intelligence (AI) system.
How AI is Revolutionizing Admissions
Every year, the New York-based Zucker School of Medicine receives around 5,000 applications. The AI system serves as an initial screener, sifting through this mountainous pile to recommend interview candidates, identify those who don’t meet baseline criteria, and flag the ‘maybes’ for further examination. This handy tech reduces the workload for human reviewers, who ultimately decide which 800 applicants will get interview offers.
Rona Woldenberg, MD, the school’s associate dean for admissions, emphasizes the efficiency of this process. “By narrowing down from 5,000 to about 1,500 or 2,000 applications,” she explains, “we can focus on the most qualified candidates.” Moreover, the use of AI helps mitigate biases that can surface in human judgment, helping to create a fairer selection process.
The Trend Among Medical Schools
Hofstra’s Zucker School isn’t alone in its adoption of AI. The NYU Grossman School of Medicine also employs AI tools for initial application screenings. As for others, the University of Cincinnati College of Medicine and The George Washington University School of Medicine and Health Sciences are in the process of developing their own AI systems, reflecting a growing trend among medical schools to leverage technology in their admissions processes.
As Laurah Turner, PhD, from UC College of Medicine puts it, “Last year we received 5,000 applicants, and we need to whittle that down to about 180.” This drastic volume means overwhelming resources are required, with some committee members spending up to 25% of their time evaluating their share of applications.
The Advantages of AI in Screening
Human reviewers do undergo training on how to evaluate applications comprehensively, considering academic credentials, clinical experiences, and personal attributes. However, as Turner notes, “When you have human reviewers, there’s going to be a lot of variation.” Factors like personal background can subtly influence how reviewers prioritize different applicant experiences.
Imagine an AI that reads applications at lightning speed and applies uniform criteria consistently. This is where AI shines, as it eliminates subjective biases that often affect human reviewers’ assessments. “AI offers a consistent review” says Marc Triola, MD, who helped pilot AI at NYU Grossman School of Medicine, “reducing the variability inherent in human screeners.”
Training AI to Understand Medical School Values
But how does one teach AI what a medical school truly values? Admissions teams collaborate with engineering departments to create algorithms that assess historical data on accepted students and their qualities. This includes sorting through easily quantifiable data like GPAs and MCAT scores, as well as identifying subtler cues that align with the school’s mission.
For example, if an applicant mentions service to underprivileged communities, the AI should recognize this as signaling qualities like empathy and commitment—even if these traits aren’t explicitly stated. The training process often involves analyzing thousands of past applications to train the AI on what traits are most likely to yield successful interview candidates.
A Look Ahead: Challenges and Opportunities
While the use of AI in admissions presents many advantages, it also raises concerns. As admissions teams utilize models based on previous human decisions, there’s a risk that existing biases may persist. “There’s going to be some amount of bias when you train an AI model,” cautions Graham Keir, MD, who advises the Zucker School on AI matters.
To tackle these concerns, some programs, like the one at Zucker, have started removing identifying details such as names and photos from applications, aiming to reduce bias in assessments. The hope is that AI can identify successful applicant patterns that could help diversify admissions moving forward.
For students anxious about AI making the crucial first call on their applications, admissions leaders reiterate that their technology is simply a tool for recommendations—not a replacement for human judgment. “AI tools produce suggestions, but human staff make the final decisions,” clarifies Koutroulis at GW SMHS.
Conclusion: Embracing the Future
The integration of AI tools in medical school admissions signals a transformative shift. While technology can enhance efficiency and reduce bias, it remains a complement to—rather than a substitute for—human insight and decision-making.
As medical schools continue to refine these systems, the hope is to balance technological advancements with the unique qualities that aspiring physicians bring to the table. The AI Buzz Hub team is excited to see where these breakthroughs take us. Want to stay in the loop on all things AI? Subscribe to our newsletter or share this article with your fellow enthusiasts.
