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W. Endrizzi

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#artificial intelligence Preprint Oct 2026

When uncertainty quantification fails: structural vulnerabilities and corrections for selective prediction under clinical class imbalance

Clinical pipelines deploying selective prediction under imbalance should adopt threshold-aligned uncertainty measures and class-stratified evaluation to reduce class-specific risk imbalance, supporting safer selective prediction under the evaluated conditions without silently excluding the most vulnerable patients.

W. Endrizzi, F. Ragni, R. Marchesi et al. · 0 citations
Review Open access Jul 2026

The landscape of artificial intelligence in neurodegenerative diseases: a systematic review.

Overall, AI research in neurodegenerative diseases suffers from significant limitations in reproducibility, data inclusivity, and clinical translatability, and a set of recommendations can be adopted to address these issues and improve reliability and downstream clinical utility.

W. Endrizzi, F. Ragni, S. Bovo et al. · 0 citations

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