Principles for Responsible AI in Medical School and Residency Selection

Artificial intelligence (AI) refers to a broad range of advanced techniques and processes that perform complex tasks, such as large-language models (LLMs), machine learning (ML), and natural language processing (NLP). Historically, simpler statistical methods have been used to analyze application data and predict performance in medical school or training.1 AI can build upon the existing body of literature and traditional techniques by using more advanced mathematical algorithms or models. This evolution can make AI a powerful tool for identifying patterns and improving decision-making in both undergraduate and graduate medical education selection processes.

The integration of AI into selection processes offers promising advancements in streamlining operations and promoting equity. For example, ML can assist in predicting applicant performance or in prioritizing applications for review. Applications can be screened in a more standardized way by using NLP to simulate expert judgment when evaluating applicant documents such as personal statements or letters of recommendation, to promote fairness and predict valued outcomes.2 LLMs can be used to improve upon a draft of interview protocols that capture competencies and characteristics important to the institution.

By thoughtfully applying AI, institutions can collectively advance 
toward more efficient, effective, fair, and informed selection processes.

Ferguson, et al.

Nevertheless, experts in medical school and residency selection are essential when making effective selection decisions. Any use of AI should be balanced with human judgment, insights, and ethical standards. What’s more, significant concerns regarding privacy, fairness, transparency, and validity of AI tools remain. It is critical that AI-driven decision-making tools be subjected to the same scrutiny applied to traditional selection methods. 

Each institution is unique, with its own mission, goals, and legal context. Therefore, tailoring the application of AI in selection processes to align with the specific needs and values of individual institutions is critical, as is review and approval by legal counsel. Institutions likely already address aspects of each principle in their process. As they consider incorporating AI into their process, they must think about how to extend their existing best practices to AI-based systems and, in some instances, address entirely new issues related to AI.

As institutions consider what is best for their process, the AAMC recommends six key principles to guide the design and use of AI-based selection systems:   

1. Balance Prediction and Understanding. Ensure that AI tools deliver insights that improve prediction and efficiency while being comprehensible and usable by the institution, aligning with its objectives and needs.
2. Protect against Algorithmic Bias. Rigorously assess and manage biases arising from historical data to ensure fair AI processes and outcomes.
3. Provide Notice and Explanation. Maintain transparency by informing applicants how AI is used and how it affects the assessment of their application.
4. Protect Data Privacy. Safeguard information with the utmost care, maintaining confidentiality at every step.
5. Incorporate Human Judgment. It is crucial to strike the appropriate balance between technology and the irreplaceable value of human judgment and ethical standards.
6. Monitor and Evaluate. Assess the outputs and outcomes of the AI system to ensure they remain fair, accurate, and aligned with institutional goals.

To learn more about each of these principles, including strategies to apply them to your practice, use the dropdown menu on the left side of the page.