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Related Experiment Video

Updated: Sep 12, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Accelerating clinical evidence synthesis with large language models.

Zifeng Wang1,2, Lang Cao1, Benjamin Danek1,2

  • 1Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign, Urbana, IL, USA.

NPJ Digital Medicine
|August 7, 2025
PubMed
Summary
This summary is machine-generated.

TrialMind, an artificial intelligence (AI) pipeline, accelerates clinical evidence synthesis by improving study search, screening, and data extraction in systematic reviews (SR). Human-AI collaboration with TrialMind significantly boosts efficiency and accuracy.

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Clinical Research Methodology

Background:

  • Systematic reviews (SR) are crucial for clinical evidence synthesis but are time-consuming.
  • Current methods for study identification, screening, and data extraction in SRs face efficiency challenges.

Purpose of the Study:

  • To introduce TrialMind, a generative AI pipeline designed to automate and enhance key tasks in SR.
  • To evaluate TrialMind's performance in study search, screening, and data extraction compared to human baselines and existing AI models.

Main Methods:

  • Development of the TrialMind AI pipeline using published SRs and clinical studies.
  • Creation of the TrialReviewBench dataset comprising 100 SRs and 2,220 clinical studies.
  • Comparative analysis of TrialMind's performance against human performance and GPT-4 for search, screening, and data extraction tasks.

Main Results:

  • TrialMind achieved high recall rates in study search (0.711-0.834) significantly outperforming human baselines (0.138-0.232).
  • TrialMind demonstrated a 1.5-2.6 fold improvement in study screening and outperformed GPT-4 in data extraction accuracy by 16-32%.
  • Human-AI collaboration using TrialMind resulted in a 71.4% increase in recall and a 44.2% reduction in screening time, with data extraction accuracy improving by 23.5% and time reduced by 63.4%.

Conclusions:

  • TrialMind shows significant promise in accelerating clinical evidence synthesis.
  • Human-AI collaboration with TrialMind enhances the efficiency and accuracy of systematic reviews.
  • Medical experts favored TrialMind-synthesized evidence, indicating its potential for reliable clinical decision-making support.