Accelerating Clinical Evidence Synthesis with Large Language Models

• URL: http://arxiv.org/abs/2406.17755v1
• Date: Tue, 25 Jun 2024 17:41:52 GMT
• Title: Accelerating Clinical Evidence Synthesis with Large Language Models
• Authors: Zifeng Wang, Lang Cao, Benjamin Danek, Yichi Zhang, Qiao Jin, Zhiyong Lu, Jimeng Sun,
• Abstract summary: We introduce TrialMind, a generative AI-based pipeline for conducting medical systematic reviews. We utilize large language models (LLMs) to drive each pipeline component while incorporating human expert oversight to minimize errors. Our results demonstrate that TrialMind significantly improves the literature review process.
• Score: 32.27109309792286
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automatic medical discovery by AI is a dream of many. One step toward that goal is to create an AI model to understand clinical studies and synthesize clinical evidence from the literature. Clinical evidence synthesis currently relies on systematic reviews of clinical trials and retrospective analyses from medical literature. However, the rapid expansion of publications presents challenges in efficiently identifying, summarizing, and updating evidence. We introduce TrialMind, a generative AI-based pipeline for conducting medical systematic reviews, encompassing study search, screening, and data extraction phases. We utilize large language models (LLMs) to drive each pipeline component while incorporating human expert oversight to minimize errors. To facilitate evaluation, we also create a benchmark dataset TrialReviewBench, a custom dataset with 870 annotated clinical studies from 25 meta-analysis papers across various medical treatments. Our results demonstrate that TrialMind significantly improves the literature review process, achieving high recall rates (0.897-1.000) in study searching from over 20 million PubMed studies and outperforming traditional language model embeddings-based methods in screening (Recall@20 of 0.227-0.246 vs. 0.000-0.102). Furthermore, our approach surpasses direct GPT-4 performance in result extraction, with accuracy ranging from 0.65 to 0.84. We also support clinical evidence synthesis in forest plots, as validated by eight human annotators who preferred TrialMind over the GPT-4 baseline with a winning rate of 62.5%-100% across the involved reviews. Our findings suggest that an LLM-based clinical evidence synthesis approach, such as TrialMind, can enable reliable and high-quality clinical evidence synthesis to improve clinical research efficiency.

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