Machine Learning (ML) technologies have been increasingly adopted in Medical Cyber-Physical Systems (MCPS) to enable smart healthcare. Assuring the safety and effectiveness of learning-enabled MCPS is challenging, as such systems must account for diverse patient profiles and physiological dynamics and handle operational uncertainties. In this paper, we develop a safety assurance case for ML controllers in learning-enabled MCPS, with an emphasis on establishing confidence in the ML-based predictions. We present the safety assurance case in detail for Artificial Pancreas Systems (APS) as a representative application of learning-enabled MCPS, and provide a detailed analysis by implementing a deep neural network for the prediction in APS. We check the sufficiency of the ML data and analyze the correctness of the ML-based prediction using formal verification. Finally, we outline open research problems based on our experience in this paper.

本文提供了一个机器学习（ML）控制器在学习式医疗物理系统（MCPS）中的安全保障方案，特别是在建立对基于ML的预测的信心方面，以胰岛素泵系统（APS）为代表的具体应用为例，通过实现深度神经网络用于APS预测，检查ML数据的充分性并使用形式化验证分析ML预测的正确性，最后概述此方案基于经验开放的研究问题。

针对学习型医疗智能物理系统开展安全保障案例的探索