PHILADELPHIA — A new AI-driven platform could allow people to interact with their medical devices by sending messages using a smartphone application and receiving feedback in real time. Participants in a proof-of-concept study undergoing monitoring for seizures could ask questions about their brain activity, receive personalized alerts, report symptoms and behaviors, and help train the system to better recognize seizure-related patterns.
The platform represents a fundamental shift from today’s medical devices, which largely collect data that is reviewed periodically by clinicians, to systems that actively collaborate with patients to manage their health, according to findings published today in Nature Medicine by researchers at the Perelman School of Medicine and the Center for Neuroengineering and Therapeutics (CNT) at the University of Pennsylvania.
“The idea is that instead of waiting for a specialist to analyze the data collected by your device, the device could detect changes and alert you immediately, like your own personal Jiminy Cricket on your shoulder,” said senior author Brian Litt, MD , a professor of Neurology, Neurosurgery, and Bioengineering. “More importantly, both the patient and the device can learn together over time. The device learns what matters to the patient, and the patient learns how their behavior affects their health.”
To test the new model for patient-device communication, researchers enrolled 13 adults undergoing continuous brain monitoring using electroencephalogram (EEG), in Penn’s Epilepsy Monitoring Unit (EMU). These individuals were admitted for monitoring for a number of reasons, including determining the source of their seizures, or as part of pre-surgical evaluation.
The participants tested a platform, constructed by a team of Penn Bioengineering PhD students, led by Zack Goldblum, Haoer Shi and Zhongchuan Xu, which consisted of three components:
Through an app on their phone, patients could ask questions about their brain activity, request visualizations of trends in their data, report symptoms, and receive alerts or surveys throughout the day. Patients could receive information such as text, charts, and other visual formats. Similarly, the device could contact the patient when it saw changes that might be of interest to the patient, and learn “on the fly” based upon patient responses.
Researchers analyzed 1,307 messages exchanged between patients and the system during the study period. Most interactions involved discussions about seizure events, sleep, mood, energy levels, and other factors that can influence seizure activity.
Patients were also surveyed three times daily about sleep, anxiety, mood, well-being, and other symptoms. Several participants said the platform helped explain patterns they had previously observed but struggled to understand.
One participant reported that the system helped “solidify what I felt to be true between some of my episodes and emotions.” Others said they valued access to sleep information and the possibility of receiving warnings before major seizures.
Beyond this communication, patients could also teach the system about their condition, noting which seizures the program correctly identified, and which were false alarms. This allowed the system to substantially improve its ability to accurately identify seizures. Clinicians could also converse with the system to review the program’s assessments and add in their own notations of true seizures and false alarms, further improving its accuracy. This was done via a smart “expert in the loop” algorithm that reduced the need for expert input to high-value annotations.
As systems become more personalized, researchers believe they will require rare clinical supervision, making them practical for long-term outpatient use.
Although the study focused on epilepsy, the team believes the platform could have broad applications for conditions that use cardiac devices, insulin pumps, and emerging treatments for neuropsychiatric disorders such as Parkinson’s disease, depression, obsessive-compulsive disorder, addiction, and cognitive disorders.
In the future, researchers envision systems that can automatically adjust treatment while communicating directly with patients about symptoms and treatment goals. The team is already preparing studies with Lauren Hammer, MD, PhD , an assistant professor of Neurology and Bioengineering, that will apply the platform to automatically tune deep brain stimulation devices used to treat Parkinson’s disease. Further work is incorporating wearables to applications for insomnia, epilepsy, brain and vital signs monitoring.
“Patients often understand aspects of their disease that sensors cannot measure directly,” Litt said. “Imagine an insulin pump that can answer questions about how a meal will affect blood sugar, or a Parkinson’s device that adjusts treatment based on how a patient feels during a walk or writing a letter. We envision medical devices becoming partners in care that learn alongside patients and help them make better decisions every day.”
Researchers believe this type of two-way communication could fundamentally change the relationship between patients and medical devices. Rather than serving as passive monitoring tools, future devices could continuously combine physiologic signals, patient feedback, and AI-driven analysis to deliver more personalized and responsive care.
The work builds on Litt’s broader effort to develop a new generation of brain-computer interfaces capable of sensing physiologic signals, communicating with patients, learning from their responses, and adapting treatment in real time, and is supported by a $5.6 million NIH Pioneer Award (DP1 NS122038-01).
Ongoing research will evaluate whether the platform can reduce seizure frequency and severity, improve quality of life, decrease emergency department visits, and lower health care utilization. Researchers also plan to expand testing into outpatient settings using newer minimally invasive EEG and wearable technologies designed for continuous monitoring during daily life.
###
Penn Medicine is one of the world’s leading academic medical centers, dedicated to the related missions of medical education, biomedical research, excellence in patient care, and community service.
The organization consists of the University of Pennsylvania Health System and Penn’s Raymond and Ruth Perelman School of Medicine , founded in 1765 as the nation’s first medical school.
The Perelman School of Medicine is consistently among the nation's top recipients of funding from the National Institutes of Health, with more than $588 million awarded in the 2024 fiscal year. Home to a proud history of “firsts,” Penn Medicine teams have pioneered discoveries that have shaped modern medicine, including CAR T cell therapy for cancer and the Nobel Prize-winning mRNA technology used in COVID-19 vaccines.
The University of Pennsylvania Health System cares for patients in facilities and their homes stretching from the Susquehanna River in Pennsylvania to the New Jersey shore. UPHS facilities include the Hospital of the University of Pennsylvania, Penn Presbyterian Medical Center, Chester County Hospital, Doylestown Health, Lancaster General Health, Princeton Health, and Pennsylvania Hospital—the nation’s first hospital, chartered in 1751. Additional facilities and enterprises include Penn Medicine at Home, GSPP Rehabilitation, Lancaster Behavioral Health Hospital, and Princeton House Behavioral Health, among others.
Penn Medicine is a $13.7 billion enterprise powered by more than 50,000 talented faculty and staff.
Nature Medicine
Observational study
People
Human-AI Collaboration for Co-Managing Epilepsy in Real Time
13-Oct-2026
The work builds on Litt’s broader effort to develop a new generation of brain-computer interfaces capable of sensing physiologic signals, communicating with patients, learning from their responses, and adapting treatment in real time, and is supported by a $5.6 million NIH Pioneer Award (DP1 NS122038-01).