# Towards Developing Safety Assurance Cases for Learning-Enabled Medical Cyber-Physical Systems

arXiv: https://arxiv.org/abs/2211.15413 · HTML: https://mlsanity.com/paper/2211.15413/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 39 references that are in the map.
- First posted on arXiv 23 Nov 2022.

## Builds on (most cited first, 3 of 3)

- [Guidance on the Assurance of Machine Learning in Autonomous Systems (AMLAS)](https://mlsanity.com/paper/2102.01564/) (2021)
- [DNNV: A Framework for Deep Neural Network Verification](https://mlsanity.com/paper/2105.12841/) (2021)
- [Design and Validation of an Open-Source Closed-Loop Testbed for Artificial Pancreas Systems](https://mlsanity.com/paper/2208.06479/) (2022)

## Built on by (most cited first, 2 of 2)

- [Integrating Neural Differential Forecasting with Safe Reinforcement Learning for Blood Glucose Regulation](https://mlsanity.com/paper/2511.12417/) (2025)
- [Towards Continuous Assurance with Formal Verification and Assurance Cases](https://mlsanity.com/paper/2511.14805/) (2025)

## Abstract (arXiv)

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.