RISED: A Pre-Deployment Safety Evaluation Framework for Clinical AI Decision-Support Systems
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Computer Science > Machine Learning
arXiv:2605.12895 (cs)
[Submitted on 13 May 2026]
Title:RISED: A Pre-Deployment Safety Evaluation Framework for Clinical AI Decision-Support Systems
Authors:Rohith Reddy Bellibatlu
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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.
| Comments: | Submission to Artificial Intelligence in Medicine (Elsevier). Open-source Python implementation at this https URL (MIT license). Synthetic evaluation cohort at this https URL (DOI: https://doi.org/10.57967/hf/8734) |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Applications (stat.AP) |
| Cite as: | arXiv:2605.12895 [cs.LG] |
| (or arXiv:2605.12895v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.12895
arXiv-issued DOI via DataCite (pending registration)
|
Submission history
From: Rohith Reddy Bellibatlu [view email][v1] Wed, 13 May 2026 02:17:13 UTC (93 KB)
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