建议在线患者病例使用人工智能进行临床试验
Joey Chan1, Qiao Jin1, Nicholas Wan1
1National Library of Medicine, National Institutes of Health, Bethesda, MD.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|February 23, 2026
概括
TrialGPT是一个人工智能框架,通过将在线患者病例与研究相匹配,提高患者获得新型治疗方法的机会,从而提高临床试验招聘率. 这种由人工智能驱动的方法显著优于传统方法.
科学领域:
- 医疗信息学 医疗信息学
- 临床试验管理 临床试验管理
- 医疗保健中的人工智能
背景情况:
- 临床试验招聘面临重大障碍,包括患者意识有限和复杂的资格标准.
- 在线平台 (社交媒体,健康社区,PubMed) 的普及为识别潜在的临床试验参与者提供了新的,尚未开发的来源.
- 传统的招聘方法往往无法有效利用这些多样化,非传统的数据源.
研究的目的:
- 评估TrialGPT的有效性,一个基于大型语言模型的框架,在将在线患者病例与合适的临床试验相匹配方面.
- 为了比较TrialGPT的性能与传统的基于关键字的临床试验招聘搜索方法.
- 评估利用非传统患者病例来源来识别临床试验参与者的潜力.
主要方法:
- 从医疗病例报告和社交媒体平台收集了50个在线患者病例.
- 采用TrialGPT框架,利用一个大型语言模型,将这些患者病例与相关的临床试验相匹配.
- 将TrialGPT的匹配性能与传统的基于关键字的搜索策略进行了比较.
主要成果:
- 与传统的基于关键字的搜索相比,TrialGPT在将患者与临床试验匹配方面表现出46%的优势.
- 平均而言,通过TrialGPT识别的患者有资格参加前10个推临床试验中的7个.
- 在外展后,从案例作者和临床试验组织者那里收到了积极的反,表明可行性和接受性.
结论:
- TrialGPT有效地利用非传统的在线患者数据来增强临床试验招聘.
- 由人工智能驱动的方法显著提高了患者的资格和获得专门护理的临床试验的机会.
- 该框架提出了一个有希望的战略,通过新的招聘途径扩大患者获得创新治疗的机会.
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