Accelerating clinical evidence synthesis with large language models.

ABSTRACT: Clinical evidence synthesis largely relies on systematic reviews (SR) of clinical studies from medical literature. Here, we propose a generative artificial intelligence (AI) pipeline named TrialMind to streamline study search, study screening, and data extraction tasks in SR. We chose published SRs to build TrialReviewBench, which contains 100 SRs and 2,220 clinical studies. For study search, it achieves high recall rates (Ours 0.711-0.834 v.s. Human baseline 0.138-0.232). For study screening, TrialMind beats previous document ranking methods in a 1.5-2.6 fold change. For data extraction, it outperforms a GPT-4's accuracy by 16-32%. In a pilot study, human-AI collaboration with TrialMind improved recall by 71.4% and reduced screening time by 44.2%, while in data extraction, accuracy increa

SUBMITTER: Wang Z 

PROVIDER: S-EPMC12331930 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature