人工智能提高了炎症性肠病临床试验的查效率:一项前性研究
Akshita Gupta1, Ryan C Ungaro1, Adam Steinlauf1
1Department of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
Inflammatory bowel diseases
|February 18, 2026
概括
使用大型语言模型 (LLM) 的新型AI工具增强了炎症性肠病 (IBD) 临床试验招聘. 与传统的医生转诊相比,人工智能工具提高了患者识别和查效率.
科学领域:
- * 临床试验招聘工作
- * 医学中的人工智能
- * 炎症性肠病 (IBD) 的研究.
背景情况:
- *临床试验对于推进IBD治疗至关重要.
- * 患者招募仍然是临床试验完成的重要瓶.
- * 目前用于识别符合条件的患者的方法可能是低效的.
研究的目的:
- *评估基于大型语言模型 (LLM) 的AI工具在改善IBD临床试验患者鉴定方面的有效性.
- * 将人工智能工具的性能与传统的医生转诊方法进行比较.
- *评估AI对临床试验查效率的影响.
主要方法:
- * 开发和实施基于LLM的AI工具来选电子医疗记录 (EMR).
- *人工智能工具被编程为IBD临床试验的特定包含和排除标准.
- * 候选患者名单由人工智能工具生成并排名进行审查.
主要成果:
- *基于LLM的AI工具显著改善了对IBD临床试验潜在合格患者的识别.
- *人工智能驱动的查与标准的医生转诊相比,显示出更高的效率.
- * 排名候选人名单有助于更简化审查过程.
结论:
- *基于LLM的AI工具提供了一个有前途的解决方案,以提高IBD临床试验的患者招募率.
- *人工智能可以优化选过程,导致更快,更有效的试验招生.
- * 这项技术有可能加速IBD研究和治疗开发.
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