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<h1>About TrialGPT</h1>
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<p>Patient recruitment is challenging for clinical trials.
We introduce TrialGPT, an end-to-end framework for zero-shot patient-to-trial matching with large language models.
TrialGPT comprises three modules: it first performs large-scale filtering to retrieve candidate trials
(TrialGPT-Retrieval); then predicts criterion-level patient eligibility (TrialGPT-Matching);
and finally generates trial-level scores (TrialGPT-Ranking).
We evaluate TrialGPT on three cohorts of 183 synthetic patients with over 75,000 trial annotations.
TrialGPT-Retrieval can recall over 90% of relevant trials using less than 6% of the initial collection.
Manual evaluations on 1,015 patient-criterion pairs show that TrialGPT-Matching achieves an accuracy of 87.3%
with faithful explanations, close to the expert performance.
The TrialGPT-Ranking scores are highly correlated with human judgments and outperform the best-competing models
by 43.8% in ranking and excluding trials. Furthermore, our user study reveals that
TrialGPT can reduce the screening time by 42.6% in patient recruitment.
Overall, these results have demonstrated promising opportunities for patient-to-trial matching with TrialGPT.
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<p>Proposed Overall Framework for Effective Patient-to-Trial Matching using Generative AI.</p>
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<td><strong>PI:</strong></td>
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<td><strong>Tech Lead:</strong></td>
<td>Qiao Jin, MD</td>
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<td><strong>Team Members:</strong></td>
<td>Yifan Yang; Joey Chan; Nick Wan; Shubo Tian, PhD</td>
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<td><strong>NEI:</strong></td>
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