ArticleNPJ digital medicine2025
The DRAGON benchmark for clinical NLP.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Orchestrated multi agents sustain accuracy under clinical-scale workloads compared to a single agent.npj health systems · 2026Article
- AI assistance in tumor multidisciplinary teams.ESMO real world data and digital oncology · 2026Review
- The Augmented Cytopathologist: A Conceptual Exploratory Narrative Review on Immersive and Vision-Language Models Tools in Digital Pathology.Journal of imaging · 2026Review
- Domain and Language adaptive pre-training of BERT models for Korean-English bilingual clinical text analysis.BMC medical informatics and decision making · 2025Article
- Leveraging open-source large language models for clinical information extraction in resource-constrained settings.JAMIA open · 2025Article
- Data Extraction From Oncology Imaging Reports by Large Language Models: A Comparative Accuracy Study.JCO clinical cancer informaticsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
28 authors.
Funding
Abstract
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.