Bayesian Uncertainty Boosts Medical AI Decision-Making

Machine learning models used in medical image analysis often struggle with ambiguous cases because they lack a reliable way to express their confidence. This research investigates the use of Monte Carlo dropout to generate an "epistemic uncertainty" signal for a multi-task chest X-ray classifier. This signal proved effective in tracking how well the model generalized and in flagging predictions that were confident but incorrect. Integrating this uncertainty signal with the model's primary prediction significantly improved the ability to detect errors. A controlled experiment showed that clinical decision-support agents could effectively utilize this uncertainty, but only when it was presented as a simple binary error-risk flag, rather than raw scores. This approach dramatically reduced confident misdiagnoses on unreliable findings. The findings suggest that while epistemic uncertainty holds valuable information beyond standard predictions, its practical utility for downstream AI agents heavily depends on how this information is communicated.