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Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
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Naive counterfactual methods in clinical settings can lead to biologically impossible scenarios, but a new sequential framework avoids this by respecting temporal dependencies and revealing clinically actionable insights.
Autonomous AI systems can achieve near-perfect accuracy while detecting zero positive cases in low-prevalence classification tasks, highlighting a critical failure mode obscured by standard metrics.
