New Research Explores Medical Devices That Adapt to Each Patient
Alex K. Jones, chair of the department of electrical engineering and computer science and the Klaus Schroder endowed professor of engineering is a principal investigator on a $1 million National Science Foundation (NSF) research project to develop implantable medical devices that learn from a patient's body and adapt automatically.
Jones leads Syracuse University’s research team on the multi-university project, which also includes Notre Dame and the University of Texas at San Antonio. Yiyu Shi, professor of computer science and engineering at Notre Dame, is the lead principal investigator. Jinjun Xiong, dean of the College of AI, Cyber and Computing at the University of Texas at San Antonio, is also a collaborator.
The project aims to improve devices such as pacemakers, implantable cardioverter defibrillators, deep brain stimulators, cochlear implants and sleep apnea implants and leverages Jones' expertise in energy-efficient computing architectures, in-memory computing, and hardware-software co-design. Those devices are typically programmed when they are implanted and often can be adjusted only during follow-up visits with a doctor, even though a patient’s condition can change from day to day and see long term trends as patients age.
“Implantable medical devices must operate reliably for years while responding to changes in a patient's physiology,” said Jones. “Our challenge is to enable increasingly sophisticated intelligence and adaptation within extremely constrained energy budgets, extending device lifetimes while improving patient outcomes and reducing the need for invasive replacement procedures.”
A central challenge is developing lightweight artificial intelligence models that can interpret physiological signals, recognize when conditions are changing, and adapt safely under uncertainty. The researchers will combine these algorithms with energy-efficient computing architectures, including in-memory computing, to minimize the energy required for continuous monitoring and learning. By co-designing the algorithms and hardware, the team aims to create implantable systems capable of evolving their behavior over time without compromising safety or device lifetime.
"This research shows what’s possible when artificial intelligence is integrated with medical device design," said Julie Hasenwinkel, interim dean of the College of Engineering and Computer Science. “Devices that can learn from and adapt to an individual patient have the potential to improve care in ways that will continue to evolve for many years to come.”
Self-evolving medical devices could help reduce health disparities by adapting to people whose individual characteristics differ from those represented in conventional device designs. Greater autonomy could also reduce the need for clinical follow-up appointments.
The grant also supports education and outreach. The project will produce open-source educational materials, support undergraduate and graduate training, and offer research experiences in artificial intelligence, embedded systems and semiconductor technologies. Outreach will include K-12 students and military veterans, and the team plans to release public datasets and benchmarking platforms.
The award was made through the NSF’s Computer Systems Research program in the Directorate for Computer and Information Science and Engineering.