Medical Imaging AI Competitions Lack Fairness

[2512.17581] Medical Imaging AI Competitions Lack Fairness Skip to main content Search Submit Donate Log in Computer Science > Computer Vision and Pattern Recognition arXiv:2512.17581 (cs) [Submitted on 19 Dec 2025 ( v1 ), last revised 28 Aug 2026 (this version, v2)] Title: Medical Imaging AI Competitions Lack Fairness Authors: Annika Reinke , Evangelia Christodoulou , Sthuthi Sadananda , A. Emre Kavur , Khrystyna Faryna , Daan Schouten , Bennett A. Landman , Carole Sudre , Olivier Colliot , Nick Heller , Sophie Loizillon , Martin Maška , Maëlys Solal , Arya Yazdan-Panah , Vilma Bozgo , Ömer Sümer , Siem de Jong , Sophie Fischer , Michal Kozubek , Tim Rädsch , Nadim Hammoud , Fruzsina Molnár-Gábor , Steven Hicks , Michael A. Riegler , Anindo Saha , Vajira Thambawita , Pal Halvorsen , Amelia Jiménez-Sánchez , Qingyang Yang , Veronika Cheplygina , Sabrina Bottazzi , Alexander Seitel , Spyridon Bakas , Alexandros Karargyris , Kiran Vaidhya Venkadesh , Bram van Ginneken , Lena Maier-Hein View PDF HTML (experimental) Abstract: Benchmarking competitions are central to the development of artificial intelligence (AI) in medical imaging, defining performance standards and shaping methodological progress. However, it remains unclear whether these benchmarks provide data that are sufficiently representative, accessible, and reusable to support clinically meaningful AI. In this work, we assess fairness along two complementary dimensions: (1) whether challenge datasets capture the diversity of real-world clinical data, and (2) whether they are accessible and legally reusable in line with the FAIR principles. To address this question, we conducted a large-scale systematic study of 249 biomedical image analysis challenges comprising 458 tasks across 19 imaging modalities. Our findings reveal limited representation across challenge datasets with respect to geographic location, imaging modalities, and problem types, raising concerns about how well current benchmarks reflect real-world clinical diversity. Despite their widespread influence, challenge datasets were frequently constrained by restrictive or ambiguous access conditions, inconsistent or non-compliant licensing practices, and incomplete documentation, limiting reproducibility and long-term reuse. Together, these shortcomings expose foundational fairness limitations in our benchmarking ecosystem and highlight a disconnect between leaderboard success and clinical relevance. Comments: Minor revision in to Nature Biomedical Engineering Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2512.17581 [cs.CV] (or arXiv:2512.17581v2 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2512.17581 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Annika Reinke [ view email ] [v1] Fri, 19 Dec 2025 13:48:10 UTC (2,850 KB) [v2] Fri, 28 Aug 2026 08:16:35 UTC (3,031 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.CV < prev | next > new | recent | 2025-12 Change to browse by: cs References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. 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