arXiv:2512.01241v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are routinely used by physicians and patients for medical advice, yet their clinical safety profiles remain poorly characterized. We present NOHARM (Numerous Options Harm Assessment for Risk in Medicine), a 1,100-task benchmark of primary care-to-specialist consultation cases to measure the frequency and severity of harm from LLM-generated medical recommendations. NOHARM covers 10 specialties, with 12,747 expert annotations for 4,249 clinical management options. Across 28 LLMs, recommendations carried the po
As LLMs are increasingly used in healthcare, the industry is proactively developing benchmarks to ensure patient safety before widespread adoption becomes irreversible. The rapid pace of AI development necessitates immediate attention to ethical and safety considerations in critical applications.
This benchmark represents a critical step towards safe and responsible AI integration in medicine, directly impacting regulatory frameworks, public trust, and the development trajectory of medical AI. It highlights the growing need for robust validation given LLM deployment in sensitive domains.
Clinical safety no longer relies solely on theoretical discussions but is now being quantified and benchmarked through comprehensive tools like NOHARM, shifting the focus towards empirical validation of LLMs in healthcare. This will likely lead to differentiated adoption based on proven safety profiles.
- · AI safety researchers
- · Healthcare providers
- · Patients
- · Ethical AI developers
- · LLMs with poor safety performance
- · Unregulated AI solutions in healthcare
- · Companies neglecting rigorous clinical validation
The NOHARM benchmark will standardize the evaluation of LLM safety in medical contexts, informing regulatory guidelines and influencing product development.
This standardization could lead to a 'safe AI' certification process for medical LLMs, significantly accelerating their adoption in trusted clinical settings.
The comprehensive dataset and findings from NOHARM could inspire similar safety benchmarks in other high-stakes domains, driving a broader push for transparent and accountable AI across industries.
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Read at arXiv cs.AI