Chan Joey, Jin Qiao, Wan Nicholas, Floudas Charalampos S, Xue Elisabetta, Lu Zhiyong
National Library of Medicine, National Institutes of Health, Bethesda, MD.
National Cancer Institute, National Institutes of Health, Bethesda, MD.
ArXiv. 2025 Apr 15:arXiv:2504.20059v1.
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, caregivers, and family members increasingly post medical cases on social media and health communities, while physicians publish case reports accessible on platforms like PubMed-collectively expanding recruitment pools beyond traditional clinical trial pathways. Recognizing this potential, we utilized TrialGPT, a framework that leverages a large language model, to match 50 online patient cases (collected from case reports and social media) to clinical trials and evaluate performance against traditional keyword-based searches. Our results show that TrialGPT outperformed traditional methods by 46%, with patients eligible, on average, for 7 of the top 10 recommended trials. Additionally, outreach to case authors and trial organizers yielded positive feedback. These findings highlight TrialGPT's potential to expand patient access to specialized care through non-traditional sources.
临床试验对于评估新疗法至关重要；然而，招募挑战——如认知度有限、复杂的合格标准和转诊障碍——阻碍了它们的成功。随着在线平台的发展，患者、护理人员和家庭成员越来越多地在社交媒体和健康社区上发布医疗病例，而医生则在PubMed等平台上发布病例报告，共同扩大了招募范围，超越了传统的临床试验途径。认识到这一潜力，我们利用了TrialGPT（一个利用大语言模型的框架），将50个在线患者病例（从病例报告和社交媒体收集）与临床试验进行匹配，并与传统的基于关键词的搜索进行性能评估。我们的结果表明，TrialGPT的表现比传统方法高出46%，患者平均有资格参加前10个推荐试验中的7个。此外，与病例作者和试验组织者的外联得到了积极反馈。这些发现凸显了TrialGPT通过非传统来源扩大患者获得专科护理机会的潜力。
