SafeMed-R1: Clinician-Audited Safety and Ethics Alignment for Medical Large Language Models
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.
Record details
Published: 27 May 2026
Source: arXiv
Category: Research
Topics: Regulation · Safety & alignment · Copyright & IP · Healthcare
Retrieved: 14 July 2026
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How to cite this record
ethics.ai (27 May 2026), “SafeMed-R1: Clinician-Audited Safety and Ethics Alignment for Medical Large Language Models,” evidence record 3601, https://ethics.ai/record/3601 (originally published by arXiv).
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