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.

该论文介绍了一种解决人工智能在医疗领域中不可信和数据有限性的挑战的新方法，提出了一种基于贝叶斯蒙特卡洛Dropout模型和核模型的方法。该模型旨在提高对小型医疗数据集的可靠性，这是阻碍人工智能在医疗领域广泛应用的关键障碍。通过利用现有的语言模型来提高效果，并与当前的工作流程无缝集成，我们展示了即使在数据有限的情况下也能显著提高可靠性的成果，为建立对基于人工智能的医疗预测的信任以及发挥其改善患者护理潜力迈出了一大步。

您会信任AI医生吗？通过核病虫害不确定性建立可靠的医疗预测