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Benchmarking vision-language models for diagnostics in emergency and critical care settings.
NPJ Digit. Med. 8:423 (2025)
The applicability of vision-language models (VLMs) for acute care in emergency and intensive care units remains underexplored. Using a multimodal dataset of diagnostic questions involving medical images and clinical context, we benchmarked several small open-source VLMs against GPT-4o. While open models demonstrated limited diagnostic accuracy (up to 40.4%), GPT-4o significantly outperformed them (68.1%). Findings highlight the need for specialized training and optimization to improve open-source VLMs for acute care applications.
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Publication type
Article: Journal article
Document type
Scientific Article
ISSN (print) / ISBN
2398-6352
e-ISSN
2398-6352
Journal
NPJ digital medicine
Quellenangaben
Volume: 8,
Issue: 1,
Article Number: 423
Publisher
Nature Publishing Group
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of AI for Health (AIH)