用AI匹配患者与临床试验，准确率比传统方法高46%。
Recommending Clinical Trials for Online Patient Cases using Artificial Intelligence
- 基于大模型的TrialGPT框架自动匹配患者与试验
- 平均每位患者可匹配7个符合条件的试验，准确率提升46%
- 适用于医疗平台、研究机构及患者支持社群
临床试验对评估新疗法至关重要，但招募困难——如认知不足、复杂入组标准和转诊障碍——制约其成效。随着在线平台发展，患者越来越多通过社交媒体和健康社区寻求支持、信息与倡导，扩大了招募池并建立了有效入组路径。我们利用以大语言模型为基底的TrialGPT框架，将50例在线患者病例（来自公开病例报告与社交网站）与临床试验进行匹配，并与传统关键词搜索对比性能。结果显示，TrialGPT在识别符合条件的试验上比传统方法高出46%，每名患者平均可匹配约7项试验。此外，向病例作者及试验负责人反馈匹配结果后，双方均给出高度积极评价，本文呈现了来自双方面的反馈意见。
原文摘要 · Abstract (English)
Clinical trials are crucial for assessing new treatments; however, recruitment challenges - such as limited awareness, complex eligibility criteria, and referral barriers - hinder their success. With the growth of online platforms, patients increasingly turn to social media and health communities for support, research, and advocacy, expanding recruitment pools and established enrollment pathways. Recognizing this potential, we utilized TrialGPT, a framework that leverages a large language model (LLM) as its backbone, to match 50 online patient cases (collected from published case reports and a social media website) to clinical trials and evaluate performance against traditional keyword-based searches. Our results show that TrialGPT outperforms traditional methods by 46% in identifying eligible trials, with each patient, on average, being eligible for around 7 trials. Additionally, our outreach efforts to case authors and trial organizers regarding these patient-trial matches yielded highly positive feedback, which we present from both perspectives.
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