Abstract
Aggregate accuracy metrics dominate the evaluation of clinical AI decision-support systems but do not detect deployment-phase failures of input reliability, subgroup equity, threshold sensitivity, or operational feasibility. We propose the RISED Framework: a five-dimension pre-deployment evaluation covering Reliability, Inclusivity, Sensitivity, Equity, and Deployability, in which each dimension is operationalized through formal sub-criteria, pre-specified pass/fail thresholds, and bias-corrected accelerated (BCa) bootstrap 95% confidence intervals combined under a Holm-Bonferroni family-wise error correction. A central demonstration is that a classifier satisfying conventional high-discrimination benchmarks can simultaneously fail input-encoding stability and threshold-shift sensitivity checks, while subgroup AUC parity remains statistically inconclusive, pointing to deployment risks that aggregate evaluation alone cannot detect. We validate this differential pass/fail pattern on a synthetic cohort and three publicly available real-world cohorts spanning 35 years of clinical data vintage, from a 1980s cardiology dataset to a 2024 nationally representative health survey, where failing dimensions differ across cohorts, providing preliminary evidence of construct validity. The Equity dimension is reframed as a proxy-dependence diagnostic rather than a stand-alone gate: any need-based fairness verdict computed against a utilization-derived proxy carries a construct-validity problem the framework surfaces explicitly, triggering a procurement requirement for an outcome-independent need measure before the gate is binding. RISED is released as an open-source Python package that supplies the quantitative verdicts existing clinical AI reporting standards require, providing a principled gateway between in-silico model validation and silent-trial clinical evaluation.
Abstract (translated)
URL
https://arxiv.org/abs/2605.12895
