人工知能を用いたオンライン患者症例に対する臨床試験の推奨

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.