LLMs Adjust Disclaimers And Referrals With Urgency
In a 2026 prospective multimodel evaluation, researchers at Charité analyzed 908 responses from four LLMs (GPT-4o, Claude Sonnet-4, Grok-3, DeepSeek-V3) to 227 authentic patient queries and classified urgency levels. All models showed statistically significant urgency-responsive patterns (P<.001), with 97% of responses advising consultation and variable rates of explicit or urgent referrals across models. The findings support safety progress but call for standardized safety measures and regulatory frameworks.
Scoring Rationale
Provides empirical evidence of urgency-adaptive safety (strong credibility), but limited generalizability across models and settings.
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