L. Julián Lechuga López

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.