First, do NOHARM: towards clinically safe large language models
📰 ArXiv cs.AI
Learn to evaluate the clinical safety of large language models using the NOHARM benchmark, crucial for medical applications
Action Steps
- Apply the NOHARM benchmark to your LLM to assess its clinical safety
- Configure your LLM to generate medical recommendations for primary care-to-specialist consultation cases
- Test the frequency and severity of harm from LLM-generated medical recommendations using NOHARM's 1,100-task benchmark
- Evaluate the performance of your LLM across 10 specialties and 12,747 expert-validated cases
- Compare your LLM's results to the NOHARM baseline to identify areas for improvement
Who Needs to Know This
Data scientists and AI researchers working on medical language models can benefit from this knowledge to ensure their models provide safe and reliable advice
Key Insight
💡 The NOHARM benchmark provides a comprehensive framework for assessing the clinical safety of large language models in medical applications
Share This
🚨 Ensure your medical LLMs do no harm! 🚨 Use the NOHARM benchmark to evaluate clinical safety #LLMs #MedicalAI #NOHARM
Key Takeaways
Learn to evaluate the clinical safety of large language models using the NOHARM benchmark, crucial for medical applications
Full Article
Title: First, do NOHARM: towards clinically safe large language models
Abstract:
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 expe
Abstract:
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 expe
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