Organizations: Shanghai Artificial Intelligence Laboratory, Shanghai, China · Joint Laboratory of Biomedical Artificial Intelligence, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China · School of Computer Science and Technology, Tongji University, Shanghai, China · University of Washington, Washington, USA · Department of Eye and Vision Sciences, University of Liverpool, Liverpool, United Kingdom · Liverpool Centre for Cardiovascular Science, University of Liverpool, Liverpool, United Kingdom · Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China · Shanghai Health Development Research Center (Shanghai Medical Information Center), Shanghai, China
Large language models(LLMs) increasingly match expert performance on licensing examinations, yet routine clinical use remains limited because governance requires auditable reasoning, safety and ethics alignment, and resilience to adversarial misuse. Here we present SafeMed-R1, trained with a traceable Clinical Trust Signals(CTS) pipeline that links each reasoning instance to clinician rubric scores and edit histories, and aligned through safety and ethics supervision and red team stress testing. SafeMed-R1 attains a macro-averaged accuracy of 79.6% across clinical benchmarks. Under adversarial safety testing, it shows the lowest aggregated risk and reduces unsafe outputs by about 3 to 5% relative to its baseline. In a paired expert study of 30 medication safety vignettes, SafeMed-R1 matches PGY1 and PGY2 residents on medical correctness and scores higher for medication safety, guideline consistency, and clinical usefulness. Collectively, these results suggest that clinician-audited supervision provenance, together with domain-tailored safety and ethics alignment, can strengthen governance-relevant evidence without relying on inference-time retrieval or citation grounding.