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Why almost all ML models for medicine are wrong-and what we need for evidence-based medical AI
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DOI：10.1016/j.ijmedinf.2026.106538.png)
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Abstract
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• Current medical ML pipelines often produce fragile evidence because of uncertain labels, inappropriate thresholds, inadequate metrics, and insufficient validation. • Evidence-based medical AI requires stronger ground truthing, calibration, uncertainty reporting, clinical utility assessment, external validation, and post-deployment monitoring.
Keywords:
Evidence-based AI
Medical machine learning
Robustness
Calibration
External validation
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