Would You Trust an AI Doctor? Building Reliable Medical Predictions with Kernel Dropout Uncertainty

arXiv:2404.10483v1 Announce Type: new
Abstract: The growing capabilities of AI raise questions about their trustworthiness in healthcare, particularly due to opaque decision-making and limited data availability. This paper proposes a novel approach to address these challenges, introducing a Bayesian Monte Carlo Dropout model with kernel modelling. Our model is designed to enhance reliability on small medical datasets, a crucial barrier to the wider adoption of AI in healthcare. This model leverages existing language models for improved effectiveness and seamlessly integrates …

abstract arxiv availability bayesian building capabilities challenges cs.lg data decision doctor dropout healthcare kernel making medical modelling novel paper predictions questions raise trust type uncertainty