arXiv Machine Learning
Detection and Validation of Hidden Subgroup Disparities in Clinical AI
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Beyond Metadata: CAPRA for Hidden Subgroup Analysis under Missing Metadata in Medical Imaging
arXiv:2607. 09102v1 Announce Type: cross Abstract: Medical imaging models are often deployed without the demographic, acquisition, and quality metadata needed for subgroup auditing.
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A Personalized Computational Framework for Assessing the Sufficiency of Partially Observed Data in Healthcare AI models
arXiv:2607. 09165v1 Announce Type: cross Abstract: Achieving early and timely diagnosis and treatment for disease is a major challenge.
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Metamorphic Testing for Clinical ML Models: A Framework Proposal and Pilot Study
arXiv:2607. 22984v1 Announce Type: cross Abstract: Machine learning models for clinical prediction tasks, such as in-hospital mortality and sepsis onset, routinely achieve high AUROC scores.
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Auditing evaluation artifacts and data leakage in large language model systems for rare disease diagnosis
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Rethinking How We Evaluate Methodological Progress in Health AI
The study re‑implements 12 AI algorithms for electronic health records within a unified framework and evaluates them on MIMIC‑IV and NWICU datasets. It compares expert‑authored clinically meaningful tasks with randomly generated tasks, finding that pairwise algorithm comparisons transfer well across task families and datasets, yet clinically meaningful tasks show stronger task‑method interactions. The results also reveal that newer algorithms do not consistently outperform older ones, with gradient‑boosted trees remaining highly competitive when combined with modern EHR representations.
arXiv Machine Learning
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Beyond Local Inspection: Global, Guideline-Grounded Evaluation of Post-hoc XAI Methods for ECG Classification
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