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ARXIV:2605.12895 · MEDICAL AI · SUBMITTED 14 MAY · 20:10 UTC · FRESHNESS FRESH
ARXIV:2605.12895MEDICAL AISUBMITTED 14 MAY · 20:10 UTCFRESHNESS FRESHRohith Reddy Bellibatlu · arXiv
A Python package for pre-deployment safety evaluation of clinical AI decision-support systems, addressing reliability, inclusivity, sensitivity, equity, and deployability.
Opportunity summary
Pain A Python package for pre-deployment safety evaluation of clinical AI decision-support systems, addressing reliability, inclusivity, sensitivity, equity, and deployability.
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Blocker Evidence unverified
A Python package for pre-deployment safety evaluation of clinical AI decision-support systems, addressing reliability, inclusivity, sensitivity, equity, and deployability. We propose the RISED Framework: a five-dimension pre-deployment evaluation covering Reliability, Inclusivity, Sensitivity, Equity, and…
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…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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,…
Medical AI moved forward this cycle; last verified May 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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A Python package for pre-deployment safety evaluation of clinical AI decision-support systems, addressing reliability, inclusivity, sensitivity, equity, and deployability.
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10.48550/arXiv.2605.12895A Python package for pre-deployment safety evaluation of clinical AI decision-support systems, addressing reliability, inclusivity, sensitivity, equity, and deployability.
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.
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PROBLEM
A Python package for pre-deployment safety evaluation of clinical AI decision-support systems, addressing reliability, inclusivity, sensitivity, equity, and deployability. We propose the RISED Framework: a five-dimension pre-deployment evaluation covering Reliability, Inclusivit...
METHOD
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-d...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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 oper...
WHY NOW
Medical AI moved forward this cycle; last verified May 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed public claims while anchored extraction refreshes.
A Python package for pre-deployment safety evaluation of clinical AI decision-support systems, addressing reliability, inclusivity, sensitivity, equity, and deployability. 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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Medical AI moved forward this cycle; last verified May 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A Python package for pre-deployment safety evaluation of clinical AI decision-support systems, addressing reliability, inclusivity, sensitivity, equity, and deployability.
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