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.

翻译：人工智能能力的增强引发了对其在医疗领域可信度的担忧，尤其在决策不透明和数据可用性有限的背景下。本文提出了一种新颖方法应对这些挑战，引入基于核建模的贝叶斯蒙特卡洛Dropout模型。该模型旨在提升小规模医疗数据集上的可靠性——这是阻碍AI在医疗领域广泛采用的关键障碍。模型通过利用现有语言模型增强效能，并能无缝融入现有工作流程。我们证明了即使在数据有限的情况下，该模型也能显著提升可靠性，为建立对AI驱动医疗预测的信任、释放其在改善患者护理方面的潜力迈出了有前景的一步。