A Unified AI Approach for Continuous Monitoring of Human Health and Diseases from Intensive Care Unit to Home with Physiological Foundation Models (UNIPHY+)

Wang, Minxiao, Kataria, Saurabh, Ni, Juntong, Buchman, Timothy G., Grunwell, Jocelyn, Mai, Mark, Jin, Wei, Clark, Matthew, Brown, Stephanie, Fundora, Michael, Sharma, Puneet, Pan, Tony, Khan, Sam, Ruchti, Timothy, Muthu, Naveen, Maher, Kevin, Bhavani, Sivasubramanium V, Hu, Xiao

arXiv.org Artificial Intelligence 

We present UNIPHY+, a unified physiological foundation model (physioFM) framework designed to enable continuous human health and diseases monitoring across care settings using ubiquitously obtainable physiological data. We propose novel strategies for incorporating contextual information during pretraining, fine-tuning, and lightweight model personalization via multi-modal learning, feature fusion-tuning, and knowledge distillation. We advocate testing UNIPHY+ with a broad set of use cases from intensive care to ambulatory monitoring in order to demonstrate that UNIPHY+ can empower generalizable, scalable, and personalized physiological AI to support both clinical decision-making and long-term health monitoring.