Medical Imaging AI Competitions Lack Fairness

• URL: http://arxiv.org/abs/2512.17581v1
• Date: Fri, 19 Dec 2025 13:48:10 GMT
• 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,
• Abstract summary: We assess fairness along two complementary dimensions: whether challenge datasets are representative of real-world clinical diversity, and whether they are accessible and legally reusable in line with the FAIR principles.<n>Our findings show substantial biases in dataset composition, including geographic location, modality, and problem type-related biases, indicating that current benchmarks do not adequately reflect real-world clinical diversity.<n>These shortcomings expose foundational limitations in our benchmarking ecosystem and highlight a disconnect between leaderboard success and clinical relevance.
• Score: 50.895929923643905
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 are representative of real-world clinical diversity, 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 241 biomedical image analysis challenges comprising 458 tasks across 19 imaging modalities. Our findings show substantial biases in dataset composition, including geographic location, modality-, and problem type-related biases, indicating that current benchmarks do not adequately 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.

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