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English(EN) Atomic Fact-Checking Increases Clinician Trust in Large Language Model Recommendations for Oncology Decision Support: A Randomized Controlled Trial

原子事实核查可提高临床医生对LLM肿瘤学推荐的信任度

作者 PulseAugur 编辑部 · [2 个来源] ·

一项涉及356名临床医生的随机对照试验发现，“原子事实核查”显著提高了临床医生对大型语言模型在肿瘤学决策支持中推荐的信任度。该方法将AI生成的治疗建议分解为可验证的声明，并链接到源指南。使用原子事实核查，表示信任的临床医生比例从26.9%上升到66.5%，其效果远大于传统的透明度方法。 AI

影响 通过提供可验证的声明，增强了临床医生对AI驱动的医疗建议的信任度，可能有助于在高风险决策制定中得到采纳。

排序理由 该集群包含一篇详细介绍AI信任度随机对照试验的学术论文。

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

原子事实核查可提高临床医生对LLM肿瘤学推荐的信任度

报道来源 [2]

  1. arXiv cs.CL TIER_1 English(EN) · Lisa C. Adams, Linus Marx, Erik Thiele Orberg, Keno Bressem, Sebastian Ziegelmayer, Denise Bernhardt, Markus Graf, Marcus R. Makowski, Stephanie E. Combs, Florian Matthes, Jan C. Peeken ·

    Atomic Fact-Checking Increases Clinician Trust in Large Language Model Recommendations for Oncology Decision Support: A Randomized Controlled Trial

    arXiv:2605.03916v1 Announce Type: new Abstract: 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?…

  2. arXiv cs.CL TIER_1 English(EN) · Jan C. Peeken ·

    Atomic Fact-Checking Increases Clinician Trust in Large Language Model Recommendations for Oncology Decision Support: A Randomized Controlled Trial

    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 clini…

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