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<p>Large language models (LLMs) are evolving rapidly and hold great promise for medical applications, yet benchmarking on real-world clinical data such as electronic health records remains limited. Most existing benchmarks rely on medical examination-style questions or PubMed-derived text, failing to capture the complexity of clinical practice, while others target specific application scenarios with limited generalizability. Here we present BRIDGE, a comprehensive multilingual benchmark comprising 87 tasks sourced from 59 real-world clinical data sources across 9 languages. It covers eight task types spanning the patient care continuum, including triage, information extraction, diagnosis, prognosis and billing coding, and involves 14 clinical specialties. We systematically evaluated 95 LLMs (including DeepSeek-R1, GPT-4o, Gemini and Qwen3) under multiple inference strategies. Results reveal substantial performance variation across model sizes, languages, natural language processing tasks and clinical specialties. Open-source LLMs can match proprietary models, while medically fine-tuned models built on older backbones often lag behind updated general-purpose LLMs. BRIDGE and its continuously updated leaderboard provide foundational resources and important references for evaluating and developing LLMs for real-world clinical text understanding.</p>

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

  • 期刊论文

  • 发表日期：

    2026年6月17日

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  • Jiageng Wu,
  • Bowen Gu,
  • Ren Zhou,
  • Kevin Xie,
  • Doug Snyder,
  • Yixing Jiang,
  • Valentina Carducci,
  • Richard Wyss,
  • Rishi J. Desai,
  • Emily Alsentzer,
  • Leo Anthony Celi,
  • Adam Rodman,
  • Sebastian Schneeweiss,
  • Jonathan H. Chen,
  • Santiago Romero-Brufau,
  • Kueiyu Joshua Lin,
  • Jie Yang

摘要

Large language models (LLMs) are evolving rapidly and hold great promise for medical applications, yet benchmarking on real-world clinical data such as electronic health records remains limited. Most existing benchmarks rely on medical examination-style questions or PubMed-derived text, failing to capture the complexity of clinical practice, while others target specific application scenarios with limited generalizability. Here we present BRIDGE, a comprehensive multilingual benchmark comprising 87 tasks sourced from 59 real-world clinical data sources across 9 languages. It covers eight task types spanning the patient care continuum, including triage, information extraction, diagnosis, prognosis and billing coding, and involves 14 clinical specialties. We systematically evaluated 95 LLMs (including DeepSeek-R1, GPT-4o, Gemini and Qwen3) under multiple inference strategies. Results reveal substantial performance variation across model sizes, languages, natural language processing tasks and clinical specialties. Open-source LLMs can match proprietary models, while medically fine-tuned models built on older backbones often lag behind updated general-purpose LLMs. BRIDGE and its continuously updated leaderboard provide foundational resources and important references for evaluating and developing LLMs for real-world clinical text understanding.