Recommending Clinical Trials for Online Patient Cases using Artificial Intelligence
Journal:
arXiv
Published Date:
Apr 15, 2025
Abstract
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.
