Feasibility-First Risk Management for Clinical AI: A Deterministic Systems Intelligence Framework for Release,...

Artificial intelligence (AI) is increasingly used to predict deterioration, classify risk, prioritise workload, support diagnosis, tailor communication and monitor patients across healthcare settings. Existing safety discussions rightly emphasise model performance, bias, explainability, regulatory approval and post-deployment monitoring. However, these domains do not fully answer a prior risk-management question: when should an AI output be released into clinical or organisational action under the active healthcare regime? This Perspective develops a Deterministic Systems Intelligence (DSI) framework for feasibility-first clinical AI risk management. The proposed admissible-state layer evaluates candidate AI outputs against data fitness, population fit, clinical actionability, workflow capacity, equity, authority, monitoring and reversibility before action is permitted. It classifies outputs into five governance states: release, restricted release, active monitoring, rollback and HOLD. HOLD denotes disciplined non-release when evidence, feasibility or safeguards are insufficient. A worked deterioration-alert example shows how the same technically plausible output may be released, restricted, monitored, rolled back or held depending on local capacity, equity and safety controls. The framework complements reporting, audit, regulatory and algorithmovigilance approaches by inserting an explicit admissibility step between AI output and healthcare action. Responsible clinical AI therefore requires not only prediction, but release readiness, monitoring and the capacity to withhold.