AI uncertainty scoring aims to boost trust in breast imaging AI

A new uncertainty-weighted learning method for breast imaging AI generates both a diagnostic prediction and an uncertainty score, flagging cases where the model’s output may be less trustworthy—aiming to guide radiologists on when a second look is needed.

• The method, uncertainty-weighted learning, integrates uncertainty directly into model training, producing a prediction and an associated uncertainty score.
• Testing on a supercomputer showed promise, but further validation across diverse populations and imaging settings is required before clinical deployment.
• Future work will extend uncertainty estimation to multimodal AI combining images with other patient data, informing tailored human-AI collaboration.

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