Towards Trustworthy AI in Healthcare: Epistemic Uncertainty Estimation for Clinical Decision Support.

ABSTRACT: Introduction: Widespread adoption of AI for medical decision-making is still hindered due to ethical and safety-related concerns. For AI-based decision support systems in healthcare settings, it is paramount to be reliable and trustworthy. Common deep learning approaches, however, have the tendency towards overconfidence when faced with unfamiliar or changing conditions. Inappropriate extrapolation beyond well-supported scenarios may have dire consequences highlighting the importance of the reliable estimation of local knowledge uncertainty and its communication to the end user. Materials and Methods: While neural network ensembles (ENNs) have been heralded as a potential solution to these issues for many years, deep learning methods, specifically modeling the amount of knowl

SUBMITTER: Lindenmeyer A 

PROVIDER: S-EPMC11856777 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature