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.
@article{arxiv.2211.15413,
title = {Towards Developing Safety Assurance Cases for Learning-Enabled Medical Cyber-Physical Systems},
author = {Maryam Bagheri and Josephine Lamp and Xugui Zhou and Lu Feng and Homa Alemzadeh},
journal= {arXiv preprint arXiv:2211.15413},
year = {2024}
}