Computational Biology Approaches to Clinical Benchmark Construction

Clinical benchmarks for AI evaluation often rely on curated case sets, but the computational and biological principles underlying case diversity are rarely made explicit. This paper applies computational approaches from population genomics and systems biology—diversity sampling, feature-space coverage, and graph-theoretic case similarity—to clinical benchmark construction. The goal is to ensure a benchmark represents the full complexity of clinical practice without requiring exhaustive case enumeration. We describe methods for: (1) computing clinical similarity between cases using structured data (patient demographics, comorbidities, imaging findings); (2) identifying "representative" or "edge" cases via clustering; (3) detecting gaps in feature coverage; (4) detecting redundancy to avoid oversampling prototypical presentations. These methods reduce benchmark size while maintaining diversity, and provide a quantitative basis for case selection rather than expert intuition alone. Application to ClinMAP-VOI v0 illustrates how computational sampling ensures the benchmark covers the clinical scenario space efficiently.