LLM医学知识强但指导真人出错，用户交互成关键短板。
Clinical knowledge in LLMs does not translate to human interactions
- 用真实用户测试，评估LLM在医疗建议中的互动表现
- 用户用LLM仅34.5%准确识别病情，44.2%正确决定处置方案
- 现有考试成绩无法预测实际人机交互中的失败
全球医疗提供者正探索使用大语言模型（LLMs）向公众提供医疗建议。尽管LLMs在医学执照考试中几乎满分，但这并不意味着其在真实场景中表现良好。我们在一项包含1,298名参与者的受控研究中测试了LLMs在十个医疗情景下辅助公众识别潜在疾病并做出处置决策的能力。参与者被随机分配至使用LLM（GPT-4o、Llama 3、Command R+）或自行选择的参考来源（对照组）。单独测试时，LLMs在94.9%的案例中正确识别疾病，平均在56.3%的案例中给出正确处置；但当用户使用这些LLMs时，仅34.5%的案例正确识别疾病，44.2%正确决定处置，表现与对照组相当。我们指出用户交互是医疗领域部署LLMs的主要挑战。现有的医学知识基准和模拟患者交互测试无法预测人类参与者中的失败。我们建议在公开部署前，系统性地进行人类用户测试以评估交互能力。
原文摘要 · Abstract (English)
Global healthcare providers are exploring use of large language models (LLMs) to provide medical advice to the public. LLMs now achieve nearly perfect scores on medical licensing exams, but this does not necessarily translate to accurate performance in real-world settings. We tested if LLMs can assist members of the public in identifying underlying conditions and choosing a course of action (disposition) in ten medical scenarios in a controlled study with 1,298 participants. Participants were randomly assigned to receive assistance from an LLM (GPT-4o, Llama 3, Command R+) or a source of their choice (control). Tested alone, LLMs complete the scenarios accurately, correctly identifying conditions in 94.9% of cases and disposition in 56.3% on average. However, participants using the same LLMs identified relevant conditions in less than 34.5% of cases and disposition in less than 44.2%, both no better than the control group. We identify user interactions as a challenge to the deployment of LLMs for medical advice. Standard benchmarks for medical knowledge and simulated patient interactions do not predict the failures we find with human participants. Moving forward, we recommend systematic human user testing to evaluate interactive capabilities prior to public deployments in healthcare.
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