将AI诊疗建议拆解为可验证条目，显著提升医生信任度。
Atomic Fact-Checking Increases Clinician Trust in Large Language Model Recommendations for Oncology Decision Support: A Randomized Controlled Trial
- 把AI建议分解为可逐条核对的医学指南引用项
- 信任度从26.9%升至66.5%，效果显著（d=0.94）
- 适合医疗AI落地，提升临床决策可信度
问题：将AI治疗建议拆解为可独立验证的声明并链接到原始指南文档（原子级事实核查），是否比传统可解释性方法更能提升医生信任？研究发现，在356名医生生成的7,476条信任评估中，原子级事实核查带来巨大信任提升（Cohen's d = 0.94），使表达信任的医生比例从26.9%提高到66.5%。传统透明性机制仅呈现剂量-响应梯度改善（d = 0.25 到 0.50）。意义：在高风险临床决策中，将AI建议分解为可验证的条目并关联原始指南，显著优于传统可解释方法，极大增强医生信任。
原文摘要 · Abstract (English)
Question: Does atomic fact-checking, which decomposes AI treatment recommendations into individually verifiable claims linked to source guideline documents, increase clinician trust compared to traditional explainability approaches? Findings: In this randomized trial of 356 clinicians generating 7,476 trust ratings, atomic fact-checking produced a large effect on trust (Cohen's d = 0.94), increasing the proportion of clinicians expressing trust from 26.9% to 66.5%. Traditional transparency mechanisms showed a dose-response gradient of improvement over baseline (d = 0.25 to 0.50). Meaning: Decomposing AI recommendations into individually verifiable claims linked to source guidelines produces substantially higher clinician trust than traditional explainability approaches in high-stakes clinical decisions.
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