Matching patients to clinical trials with large language models

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...

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Bibliographic Details
Published in:	Nature communications Vol. 15; no. 1; pp. 9074 - 14
Main Authors:	Jin, Qiao, Wang, Zifeng, Floudas, Charalampos S., Chen, Fangyuan, Gong, Changlin, Bracken-Clarke, Dara, Xue, Elisabetta, Yang, Yifan, Sun, Jimeng, Lu, Zhiyong
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 18.11.2024 Nature Publishing Group Nature Portfolio
Subjects:	631/114/1305 692/308/2779 692/700 Algorithms Annotations Clinical trials Clinical Trials as Topic Criteria Humanities and Social Sciences Humans Language Large language models Matching multidisciplinary Patient Selection Ranking Recruitment Retrieval Science Science (multidisciplinary)
ISSN:	2041-1723, 2041-1723
Online Access:	Get full text
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