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Tandon School of Engineering, New York University
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Data-driven priors, leveraging cross-modal similarity and modality-specific corruptions, can substantially improve both the accuracy and reliability of uncertainty estimates in multimodal clinical risk prediction.
Selective prediction, a proposed safeguard for AI in clinical settings, can backfire dramatically due to class-dependent miscalibration, leading to worse performance than simply trusting the model.
