RxSafeBench: A reality check on AI medication safety
AI assistants are getting better at healthcare tasks—but are they safe when it comes to medications? A new study introduces RxSafeBench, a realistic test to see whether large language models (LLMs) can spot medication risks during simulated doctor-patient chats.
Using a curated RxRisk DB (6,725 contraindications, 28,781 drug interactions, 14,906 indication-drug pairs) and a two-stage clinical review, the team built 2,443 high-quality consultation scenarios with embedded risks. Leading open and proprietary LLMs were asked to choose safe treatments based on each patient's context.
- Result: Current LLMs often miss contraindications and interactions—especially when the danger is implied, not stated.
- Implication: Better prompting and task-specific tuning help, but safety gaps remain.
- Contribution: RxSafeBench offers the first comprehensive benchmark for medication safety in LLMs.
Why it matters: Reliable AI should protect patients from harmful prescriptions, not just provide quick answers. This benchmark gives researchers and developers a common yardstick to build—and verify—safer clinical AI.
Paper: http://arxiv.org/abs/2511.04328v1
Paper: http://arxiv.org/abs/2511.04328v1
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