在瘤学临床试验中进行预查和查工具的统一框架
Denis Horgan1,2, Joe Paulson3,4, Arturo Loaiza-Bonilla5,6
1European Alliance for Personalised Medicine, Brussels, Belgium.
NPJ precision oncology
|January 29, 2026
概括
复杂的瘤学临床试验标准阻碍了招生. 大型语言模型和混合方法对改善患者招募和获得新型癌症治疗方法的机会充满希望.
科学领域:
- 在瘤学瘤学.
- 临床试验 临床试验
- 人工智能的人工智能
背景情况:
- 由于复杂的资格标准,生物标志物分层和碎片化数据,瘤学临床试验招生面临挑战.
- 这些因素导致招聘时间延长和患者参与率降低.
研究的目的:
- 对瘤学临床试验的当代预查和查方法进行审查.
- 评估人工智能 (AI) 的优势和局限性,特别是大型语言模型 (LLM),以改善试验招聘.
- 确定有效的策略,以提高试验匹配效率,代表性和获得试验治疗的机会.
主要方法:
- 审查当前的预选和选方法,包括手动流程,数字工具和支持人工智能的方法.
- 评估大型语言模型 (LLM) 策略,如检索增强和域适应的方法.
- 评估混合框架,将自动查与临床医生监督相结合.
主要成果:
- 手动工作流程和现有的数字工具在解决现代临床试验查的复杂性方面存在局限性.
- 大型语言模型为试验招聘的可扩展性,准确性和公平性挑战提供了潜在的解决方案.
- 将自动查与人类监督相结合的混合框架显示出最大的有效性.
结论:
- 将自动查与临床医生监督相结合的混合框架是改善瘤学试验匹配的最有效方法.
- 这些综合方法增强了患者的代表性,并确保及时获得临床癌症治疗.
- 优化查过程对于推进瘤学研究和患者护理至关重要.
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