发表机构
National Library of Medicine, National Institutes of Health; University of Illinois Chicago; University of Illinois Urbana-Champaign; National Cancer Institute, National Institutes of Health（美国国立卫生研究院国立医学图书馆; 伊利诺伊大学芝加哥分校; 伊利诺伊大学厄巴纳-香槟分校; 美国国立卫生研究院国家癌症研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出AI辅助临床试验推荐系统TrialGPT 2.0，经多中心评估可提升临床医生效率、增加患者入组机会，还发布了配套数据集NIH-TrialBench以支持研究可重复性。
AI 中文摘要
临床试验对于推进癌症治疗和药物开发至关重要，但许多试验因患者入组不足而失败。尽管人们对使用人工智能支持患者招募的兴趣日益浓厚，现有系统大多仅执行资格评估，且很少在真实世界肿瘤学工作流程中进行评估。在此，我们提出TrialGPT 2.0，一款为真实世界部署设计的AI辅助临床试验推荐系统。该系统不仅会判断患者是否可能符合条件，还会根据患者当前的临床需求和当地工作流程优先级评估哪些试验值得进一步考虑，并提供结构化、可检查的解释供专家审查。重要的是，我们在多个以肿瘤学为重点的环境中对TrialGPT 2.0进行了回顾性和前瞻性评估，涵盖政府、学术癌症中心、患者倡导组织以及NIH转诊工作流程。在包含288个病例的回顾性多中心队列中，TrialGPT 2.0在其前10项推荐中为约91%的病例检索到至少1项临床医生推荐的试验，同时将临床医生的筛查时间减少了55.0%。在嵌入活跃的精准肿瘤学肿瘤委员会的为期六个月的前瞻性评估中，TrialGPT 2.0发现了常规工作流程遗漏的额外试验机会，使患者参与临床试验的机会增加了90.9%。为支持科学可重复性，我们还推出了NIH-TrialBench，这是一个由临床医生编写的数据集，包含来自11个NIH研究所和中心的126种不同的合成患者 vignette（病例简介）及匹配场景。这些结果共同证明，AI辅助临床试验匹配可提高临床医生效率，识别常被忽视的试验机会，最终有助于扩大并加速癌症试验的入组。
英文摘要
Clinical trials are essential for advancing cancer care and drug development, but many fail because of insufficient patient enrollment. While there is growing interest in using AI to support patient recruitment, existing systems largely perform eligibility assessment alone and have rarely been evaluated in real-world oncology workflows. Here we present TrialGPT 2.0, an AI-assisted clinical trial recommendation system designed for real-world deployment. Rather than asking only whether a patient may qualify, the system also assesses which trials warrant further consideration given the patient's current clinical needs and local workflow priorities, and provides structured, inspectable explanations for expert review. Importantly, we evaluated TrialGPT 2.0 retrospectively and prospectively across multiple oncology-focused settings, spanning government, academic cancer-center, patient-advocacy, and NIH referral workflows. In retrospective multicenter cohorts comprising 288 cases, TrialGPT 2.0 retrieved at least one clinician-recommended trial in its top 10 recommendations for approximately 91% of cases while reducing clinician screening time by 55.0%. In a six-month prospective evaluation embedded in an active precision oncology tumor board, TrialGPT 2.0 contributed additional trial opportunities missed by the routine workflow, expanding patient access to clinical trial participation by 90.9%. To support scientific reproducibility, we also introduce NIH-TrialBench, a clinician-authored dataset comprising 126 diverse synthetic patient vignettes and matching scenarios from 11 NIH Institutes and Centers. Together, these results support the value of AI to assist clinical trial matching by improving clinician efficiency and identifying frequently overlooked trial opportunities, ultimately helping to expand and accelerate accrual to cancer trials.
Comments43 pages, 12 figures
