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BRIDGE: benchmarking large language models for understanding real-world clinical practice texts.

Jiageng Wu1, Bowen Gu1, Ren Zhou2

  • 1Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

Nature Biomedical Engineering
|June 17, 2026
PubMed

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Summary

A new benchmark, BRIDGE, evaluates large language models (LLMs) on real-world clinical data. It shows performance varies, with open-source LLMs matching proprietary ones and updated general models outperforming older specialized ones.

Area of Science:

  • Artificial Intelligence
  • Medical Informatics
  • Natural Language Processing

Background:

  • Large language models (LLMs) show potential in medicine, but real-world clinical data benchmarking is lacking.
  • Existing benchmarks often use artificial questions or limited datasets, not reflecting clinical practice complexity.
  • There's a need for comprehensive evaluation of LLMs on diverse, real-world clinical text.

Purpose of the Study:

  • To introduce BRIDGE, a multilingual benchmark for evaluating LLMs on real-world clinical data.
  • To assess the performance of various LLMs across different languages, tasks, and clinical specialties.
  • To provide a resource for advancing LLM development in clinical text understanding.

Main Methods:

  • Developed BRIDGE, a benchmark with 87 tasks from 59 real-world clinical data sources in 9 languages.

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  • Included 8 task types (e.g., triage, diagnosis, coding) across 14 clinical specialties.
  • Evaluated 95 LLMs using multiple inference strategies.

    • Main Results:

    • Significant performance variations were observed across LLM sizes, languages, tasks, and specialties.
    • Open-source LLMs demonstrated performance comparable to proprietary models.
    • Medically fine-tuned models on older architectures underperformed updated general-purpose LLMs.

    Conclusions:

    • BRIDGE offers a robust evaluation framework for LLMs in clinical settings.
    • The benchmark highlights the need for continuous development and evaluation of LLMs for real-world medical applications.
    • BRIDGE and its leaderboard serve as crucial resources for the research community.