Abstract:

Medicine 3.0 is on the horizon, heralding a new era where artificial intelligence and wearable technologies converge to transform healthcare. Devices such as smartwatches already provide real-time health alerts and facilitate early detection of cardiac conditions—highlighting the life-saving potential of AI. With continuous advancements in sensing and machine learning, the vision of intelligent, affordable, and accessible medical devices becoming commonplace is quickly becoming a reality.

 

This talk examines the evolving frontier of AI-enabled medical sensing, emphasizing neural network architectures designed for edge deployment. In particular, it explores the potential of Weightless Neural Networks (WNNs)—a lightweight, energy-efficient computing model ideally suited for wearable health monitoring systems. Key developments will be presented, including a flexible arrhythmia detection device fabricated on a plastic thin-film substrate that utilizes WNNs to deliver high diagnostic accuracy. The session will offer insights into how AI is advancing preventive healthcare and catalyzing innovation in next-generation medical diagnostics.

Eugene B. John is a Professor in the Department of Electrical and Computer Engineering at the University of Texas at San Antonio (UTSA), where he also directs the Laboratory for Energy Efficient Computing and Machine Learning. He earned his Ph.D. in Electrical Engineering from Pennsylvania State University.

Dr. John’s research interests include energy-efficient computing, AI/machine learning hardware, ultra-low-power architecture for implantable cardiac devices, computer architecture, power- and performance-aware system design, secure computing, and low-power integrated circuits and systems. He has published over 170 research articles and holds 9 issued U.S. patents. His research has been supported by leading agencies and organizations, including the National Science Foundation (NSF), National Institutes of Health (NIH), Semiconductor Research Corporation (SRC), Army Research Office (ARO), Air Force Office of Scientific Research (AFOSR), Texas Higher Education Coordinating Board (THECB-ATP), Intel, IBM, Xilinx, and UTSA’s Center for Infrastructure Assurance and Security (CIAS).

Dr. John is a recipient of the prestigious University of Texas System Regents’ Outstanding Teaching Award and is a member of the National Academy of Inventors (NAI). He served as an Associate Editor for IEEE Transactions on Sustainable Computing (2019–2023) and currently serves as an Associate Editor for ACM Computing Surveys.