On Device AI to Better Mobile and Implantable Devices in Healthcare

Abstract

The increasing prevalence of chronic diseases, an aging population, and a shortage of healthcare professionals have prompted the widespread adoption of mobile and implantable devices to effectively manage various health conditions. In recent years, there is growing interest to leverage the rapid advances in artificial intelligence (AI) to enhance the performance of these devices, resulting in better patient outcomes, reduced healthcare costs, and improved patient autonomy. Due to privacy, security, and safety considerations, inferences must often be done on the edge, with limited hardware resources. This is compounded by inter-patient and intra-patient variability, heavy dependence on medical domain knowledge, and a lack of diversified training data. In this talk, we will demonstrate how techniques such as hardware and neural architecture co-design, personalized meta-learning, and fairness-aware pruning can transform the landscape of mobile and implantable devices. Additionally, we will showcase the world's first smart ImplantableCardioverter Defibrillator (ICD) design enabled by our research