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<p>Artificial Intelligence can mitigate the global shortage of medical diagnostic personnel but requires large-scale annotated datasets to train clinical algorithms. Natural Language Processing (NLP), including Large Language Models (LLMs), shows great potential for annotating clinical data to facilitate algorithm development but remains underexplored due to a lack of public benchmarks. This study introduces the DRAGON challenge, a benchmark for clinical NLP with 28 tasks and 28,824 annotated medical reports from five Dutch care centers. It facilitates automated, large-scale, cost-effective data annotation. Foundational LLMs were pretrained using four million clinical reports from a sixth Dutch care center. Evaluations showed the superiority of domain-specific pretraining (DRAGON 2025 test score of 0.770) and mixed-domain pretraining (0.756), compared to general-domain pretraining (0.734, <i>p</i> &lt; 0.005). While strong performance was achieved on 18/28 tasks, performance was subpar on 10/28 tasks, uncovering where innovations are needed. Benchmark, code, and foundational LLMs are publicly available.</p>

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The DRAGON benchmark for clinical NLP

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月17日

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  • Joeran S. Bosma,
  • Koen Dercksen,
  • Luc Builtjes,
  • Romain André,
  • Christian Roest,
  • Stefan J. Fransen,
  • Constant R. Noordman,
  • Mar Navarro-Padilla,
  • Judith Lefkes,
  • Natália Alves,
  • Max J. J. de Grauw,
  • Leander van Eekelen,
  • Joey M. A. Spronck,
  • Megan Schuurmans,
  • Bram de Wilde,
  • Ward Hendrix,
  • Witali Aswolinskiy,
  • Anindo Saha,
  • Jasper J. Twilt,
  • Daan Geijs,
  • Jeroen Veltman,
  • Derya Yakar,
  • Maarten de Rooij,
  • Francesco Ciompi,
  • Alessa Hering,
  • Jeroen Geerdink,
  • Henkjan Huisman,
  • Max J. J. de Grauw,
  • Leander van Eekelen,
  • Bram de Wilde,
  • Quintin van Lohuizen,
  • Michelle Stegeman,
  • Karlijn Rutten,
  • Inge M. E. Smit,
  • Gijs Stultiens,
  • Christiaan G. Overduin,
  • Matthieu J. C. M. Rutten,
  • Ernst Th. Scholten,
  • Rachel S. van der Post,
  • Katrien Grünberg,
  • Shoko Vos,
  • Elise M. G. Taken,
  • Iris D. Nagtegaal,
  • Anne Mickan,
  • Miriam Groeneveld,
  • Paul K. Gerke,
  • James A. Meakin,
  • M. G. Looijen-Salamon,
  • Tijmen L. M. de Haas,
  • Fabian Hoitsma,
  • Marina D’Amato,
  • Maarten de Rooij

摘要

Artificial Intelligence can mitigate the global shortage of medical diagnostic personnel but requires large-scale annotated datasets to train clinical algorithms. Natural Language Processing (NLP), including Large Language Models (LLMs), shows great potential for annotating clinical data to facilitate algorithm development but remains underexplored due to a lack of public benchmarks. This study introduces the DRAGON challenge, a benchmark for clinical NLP with 28 tasks and 28,824 annotated medical reports from five Dutch care centers. It facilitates automated, large-scale, cost-effective data annotation. Foundational LLMs were pretrained using four million clinical reports from a sixth Dutch care center. Evaluations showed the superiority of domain-specific pretraining (DRAGON 2025 test score of 0.770) and mixed-domain pretraining (0.756), compared to general-domain pretraining (0.734, p < 0.005). While strong performance was achieved on 18/28 tasks, performance was subpar on 10/28 tasks, uncovering where innovations are needed. Benchmark, code, and foundational LLMs are publicly available.