arXiv:2605.28338v1 Announce Type: new Abstract: 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.
The increasing sophistication of LLMs is pushing them toward clinical application, necessitating robust safety and ethics frameworks to overcome existing governance barriers.
This development addresses key hurdles for AI integration into critical sectors like healthcare, demonstrating a pathway for high-stakes AI applications to achieve auditable reasoning and safety.
The explicit methodology for clinician-audited safety and ethics alignment provides a template for developing trustworthy AI in medicine, shifting focus from raw performance to governance and real-world applicability.
- · AI developers in healthcare
- · Healthcare providers
- · Patients
- · Regulatory bodies
- · LLM developers without clear safety frameworks
- · Clinical workflows resistant to AI integration
Medical LLMs will begin to see wider adoption in clinical support roles as trust and safety frameworks mature.
The 'Clinical Trust Signals' pipeline could become a standard for AI validation in other high-risk professional domains.
This could accelerate the creation of specialized, auditable AI agents, transforming diagnostics and treatment planning across healthcare systems globally.
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Read at arXiv cs.AI