Guide to Navigating AI Use Cases in Medical Education Selection
How to Use This Guide
The increasing volume of medical school and residency applications creates challenges as well as new opportunities for maintaining thorough and fair evaluation at scale. This guide presents a set of use cases on navigating artificial intelligence (AI) in the medical education selection process. The use cases were developed by the AAMC in closely working with medical education experts.
Finding Your Solution
| If your priority is … | Consider … |
|---|---|
|
Evaluating professional competencies consistently across high volumes |
Use Case 1: Competency-Based Application Review |
|
Identifying strong interview candidates using historical, data-driven methods |
Use Case 2: Data-Driven Applicant Interview Selection |
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Understanding applicant backgrounds systematically with legal awareness |
Use Case 3: LLM-Assisted Socioeconomic Context Analysis |
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Combining quantitative metrics with qualitative insights for efficiency |
Use Case 4: Predictive Scoring With Smart Summaries |
What to Expect
Each use case below follows a structured format:
- Challenge. The specific selection problem.
- Solution. How AI addresses it.
- How it Works. Implementation steps and examples.
- Key Takeaways. Core benefits, requirements, and challenges.
- Bottom Line. Best-fit scenarios and resource needs.
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