{"data":{"id":"d3c2554d-20c0-4e6f-9756-3e2692657c8f","title":"Offline Inverse Constrained Reinforcement Learning for Safe-Critical Decision Making in Healthcare","summary":"This research addresses how to make reinforcement learning (RL, where AI systems learn to make decisions by trial and error) safer for healthcare by proposing a method called Constraint Transformer that learns safety rules from historical medical records instead of requiring real-time interaction. The system uses a causal attention mechanism (a technique that identifies which past events matter most) and a generative world model (a simulation tool) to identify unsafe treatment decisions and improve patient outcomes while reducing harmful behaviors.","solution":"N/A -- no mitigation discussed in source.","labels":["research","safety"],"sourceUrl":"http://ieeexplore.ieee.org/document/11168453","publishedAt":"2025-09-17T13:18:08.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":[],"issueType":"research","affectedPackages":null,"affectedVendors":[],"affectedVendorsRaw":[],"classifierModel":"claude-haiku-4-5-20251001","classifierPromptVersion":"v3","cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"patchAvailable":null,"disclosureDate":"2025-09-17T13:18:08.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"moderate","impactType":["safety"],"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.92,"researchCategory":"peer_reviewed","atlasIds":null}}