  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
with current workflows. We demonstrate significant improvements in reliability,
even with limited data, offering a promising step towards building trust in
AI-driven medical predictions and unlocking its potential to improve patient
care.
