  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.
