A new study published on arXiv investigated how clinician input influences the recommendations of large language models (LLMs) in clinical settings. Researchers found that clinician reasoning significantly increased the accuracy of AI-generated differential diagnoses and recommendations, but also amplified harmful suggestions when the input was misleading. The study tested eight different AI models, including GPT-5, Claude Sonnet 4.5, and Gemini 3 Flash, using curated medical case records. Findings suggest that while expert clinician input improves AI performance, adversarial input poses a vulnerability that requires mitigation strategies, such as inference-time prompting, to ensure safety and robustness. AI
IMPACT Highlights the critical need for robust safety measures and evaluation metrics for LLMs in healthcare, as clinician interaction can both improve and degrade AI performance.
RANK_REASON Research paper detailing AI model behavior in a specific domain. [lever_c_demoted from research: ic=1 ai=1.0]
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