THE PROBLEM: AN OVERWHELMING SCREENING PROCESS The residency application review process in the United States has become a burdensome and imperfect gatekeeper for medical students, imposing heavy time demands and leaving both applicants and programs vulnerable to bias. Programs routinely receive hundreds of applications per cycle, each often spanning 30–60 pages. The cost is substantial: physician-educators and faculty must divert clinical and teaching time to the exhaustive review of applications, reducing their availability for core educational activities and patient care.1,2 At the same time, the sheer volume and complexity of applications make truly fair and consistent evaluation difficult; program directors or reviewers often rely on quick filters or cutoff scores rather than a full, detailed evaluation of every publication and every experience on an applicant’s application. The subjective nature of human judgment only worsens the problem. Decisions potentially fluctuate depending on the reviewer’s mood, the order of applications seen (“ah, this one is much better than the last”), and local anchoring effects. With limited bandwidth, even the most conscientious programs are at risk of overlooking strong candidates or making decisions that are variably reliable. Indeed, existing literature describes how traditional screening tools are “fraught with bias” and, despite enormous investment, do not reliably deliver equitable outcomes.3 ARTIFICIAL INTELLIGENCE HAS ALREADY CHANGED EVERYTHING ELSE: WHY NOT RESIDENCY SELECTION? Artificial intelligence (AI) has rapidly emerged as one of the most transformative technologies of our time, reshaping how we make decisions, allocate time, and manage complexity across nearly every industry. In finance, AI systems analyze millions of transactions in seconds to detect fraud with greater accuracy than human auditors. In aviation, AI supports flight scheduling and safety monitoring, reducing human error and improving operational efficiency. In health care, AI algorithms now assist in radiology, pathology, and diagnostics, flagging subtle abnormalities faster and often more accurately than clinicians working alone.4 The common thread across these advances is efficiency and consistency: AI handles the repetitive and data-heavy tasks that exhaust human reviewers, allowing experts to focus on judgment, nuance, and empathy. When applied thoughtfully, AI systems can help standardize evaluation, mitigate bias through structured criteria, and create a more transparent and equitable process. It is therefore unsurprising that graduate medical education programs are beginning to explore how similar technologies could revolutionize residency application screening, transforming a slow, subjective process into one that is faster, fairer, and more systemic. THE OPPORTUNITY AND RESPONSIBILITY OF USING AI IN RESIDENCY SELECTION But with this opportunity comes responsibility. The Association of American Medical Colleges has made it clear that the path forward is not simply adopting AI, but adopting it thoughtfully. Their published framework outlines criteria programs should use when evaluating AI screening tools, emphasizing principles such as transparency, data privacy, reduction of algorithmic bias, the need for interpretable outputs, and the requirement that AI augments, not replaces, human judgment.5 The guidance also stresses ongoing monitoring and validation so that systems evolve with institutional priorities rather than becoming static black boxes. These safeguards are essential, especially in high-stakes educational decisions where fairness and trust are foundational. If implemented within these parameters, AI could be more than a technological upgrade; it could represent a meaningful step toward a selection process that is both more humane and more rigorous. LOOKING AHEAD: A FUTURE WE CANNOT IGNORE Whether we feel ready or not, the trajectory is clear: AI will play a central role in every aspect of our lives, and residency selection is not an exception. In 5–10 years, it is unlikely that faculty will still be scrolling through individual PDFs or spending late evenings manually going through tens of pages per applicant. Instead, AI systems will pre-analyze experiences and achievements, identify alignment with program values, highlight standout experiences, and help ensure fair and consistent evaluation across every candidate. Close your eyes for a moment and imagine that future: a world where what once took days or even weeks now takes minutes, where the reviewing experience is more thoughtful, more equitable, and far less exhausting. The pace of medicine, technology, and education is accelerating, and the systems we rely on must evolve with it. AI is not just a tool—it is the next chapter in how we evaluate talent, potential, and readiness for the profession. We can resist it, or we can shape it, but its arrival is inevitable. The sooner we engage with it responsibly, the sooner we can harness its power to strengthen, not replace, the humanity at the heart of medical education. DISCLOSURE The author discloses a leadership role as founder and CEO of RankRx, a company using AI to screen residency applications.
Malke Asaad (Sun,) studied this question.