The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous
arXiv:2605.13046v1 Announce Type: new Abstract: Mental health disorders affect millions worldwide, and healthcare systems are increasingly overwhelmed by the volume of clinical
arXiv:2608.13618v1 Announce Type: new Abstract: A single clinical algorithm can deliver unequal accuracy across patient groups, and concern about such disparity has grown as
arXiv:2605.11398v1 Announce Type: new Abstract: We introduce AcuityBench, a benchmark for evaluating whether language models identify the appropriate urgency of care from user
arXiv:2606.31616v1 Announce Type: new Abstract: Medical Artificial Intelligence (AI) is widely expected to transform clinical practice, yet the decision-making processes of many
arXiv:2604.22167v1 Announce Type: cross Abstract: Language models are increasingly capable and are being rapidly deployed on a population-level scale. As a result, the safety of
arXiv:2605.13848v1 Announce Type: new Abstract: Agentic LLM frameworks that rely on prompted orchestration, where the model itself determines workflow transitions, often suffer
arXiv:2604.08590v1 Announce Type: cross Abstract: We present AlphaLab, an autonomous research harness that leverages frontier LLM agentic capabilities to automate the full
arXiv:2607.15992v1 Announce Type: new Abstract: Over the past decade, responsible AI (RAI) has produced a substantial body of practice for identifying and mitigating the risks AI
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