The DRAGON benchmark for clinical NLP
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.
Authors
- Francesco Ciompi
- Joeran S. Bosma (ORCID: https://orcid.org/0000-0002-6315-124X)
- Max de Grauw (ORCID: https://orcid.org/0009-0002-6671-2142)
- Anindo Saha (ORCID: https://orcid.org/0000-0002-5091-9862)
- Jasper J. Twilt (ORCID: https://orcid.org/0000-0002-9673-6770)
- Ward Hendrix (ORCID: https://orcid.org/0000-0002-3357-2959)
- Witali Aswolinskiy (ORCID: https://orcid.org/0000-0003-0057-3039)
- Bram de Wilde (ORCID: https://orcid.org/0000-0003-1890-8714)
- Joey Spronck (ORCID: https://orcid.org/0000-0001-9653-9542)
- Stefan J. Fransen (ORCID: https://orcid.org/0000-0002-5454-5052)
- Daan Geijs (ORCID: https://orcid.org/0000-0003-3984-1766)
- R. Johanes Andre
- Megan Schuurmans (ORCID: https://orcid.org/0000-0002-4939-6187)
- HenkJan J. Huisman (ORCID: https://orcid.org/0000-0001-6753-3221)
- Christian Roest (ORCID: https://orcid.org/0000-0002-3575-4465)
- Koen Dercksen
- Leander van Eekelen (ORCID: https://orcid.org/0000-0002-6728-6352)
- Jeroen Geerdink (ORCID: https://orcid.org/0000-0001-6718-6653)
- Constant R. Noordman (ORCID: https://orcid.org/0000-0003-4358-0087)
- Derya Yakar (ORCID: https://orcid.org/0000-0002-4920-9441)
- Natália Alves (ORCID: https://orcid.org/0000-0002-7034-0137)
- Alessa Hering (ORCID: https://orcid.org/0000-0002-7602-803X)
- Jeroen Veltman (ORCID: https://orcid.org/0000-0002-6824-3987)
- Maarten de Rooij (ORCID: https://orcid.org/0000-0001-7257-7907)
- Luc Builtjes
- Judith Lefkes
- Mar Navarro-Padilla
Institutions
- University Medical Center Groningen (NL)
- Radboud University Nijmegen (NL)
- Radboud University Medical Center (NL)
- The Netherlands Cancer Institute (NL)
- Oncode Institute (NL)
- Ziekenhuis Groep Twente (NL)
- Jeroen Bosch Ziekenhuis (NL)
Publication Details
- Journal
- npj Digital Medicine
- Published
- 2025-05-17
- DOI
- https://doi.org/10.1038/s41746-025-01626-x
- Citations
- 9
- Primary Topic
- Topic Modeling
- Type
- article
- Field-Weighted Citation Impact
- 22.33
Funders
- Health~Holland
- European Commission
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek
- Horizon 2020
- Radboud Institute for Health Sciences