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Updated: Sep 13, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Recommending Clinical Trials for Online Patient Cases using Artificial Intelligence.

Joey Chan1, Qiao Jin1, Nicholas Wan1

  • 1National Library of Medicine, National Institutes of Health, Bethesda, MD.

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Summary

This study introduces TrialGPT, an AI tool that improves clinical trial recruitment by matching online patient cases to trials. TrialGPT significantly outperforms traditional methods, enhancing patient access to new treatments.

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Area of Science:

  • Medical Informatics
  • Clinical Trial Management
  • Artificial Intelligence in Healthcare

Background:

  • Clinical trial recruitment faces significant challenges, including limited awareness and complex criteria.
  • Online platforms offer a growing, yet largely untapped, resource for identifying potential clinical trial participants.
  • Traditional recruitment methods often fail to leverage the wealth of information available in online patient cases and physician-published reports.

Purpose of the Study:

  • To evaluate the efficacy of TrialGPT, a large language model-based framework, in matching online patient cases to suitable clinical trials.
  • To compare the performance of TrialGPT against traditional keyword-based search methods for clinical trial recruitment.
  • To assess the potential of non-traditional data sources for expanding patient access to clinical trials.

Main Methods:

  • A framework named TrialGPT was developed, utilizing a large language model to analyze online patient cases.
  • Fifty online patient cases were collected from social media and published case reports.
  • TrialGPT's matching performance was evaluated against conventional keyword-based searches for clinical trial eligibility.

Main Results:

  • TrialGPT demonstrated a 46% improvement in matching performance compared to traditional methods.
  • On average, patients using TrialGPT were eligible for 7 out of the top 10 recommended clinical trials.
  • Positive feedback was received from both case authors and clinical trial organizers regarding the TrialGPT approach.

Conclusions:

  • TrialGPT effectively leverages non-traditional online patient data for clinical trial recruitment.
  • This AI-driven approach significantly enhances patient eligibility and access to clinical trials.
  • TrialGPT presents a promising strategy for overcoming traditional recruitment barriers and expanding access to specialized medical care.