A Unified AI Approach for Continuous Monitoring of Human Health and Diseases from Intensive Care Unit to Home with Physiological Foundation Models (UNIPHY+)
This work addresses the need for adaptable health monitoring systems for patients in various settings, from intensive care to home, but appears incremental as it builds on existing foundation model concepts with novel strategies.
The paper tackles the problem of continuous human health monitoring across care settings by proposing UNIPHY+, a unified physiological foundation model framework, and demonstrates its potential for generalizable, scalable, and personalized AI applications.
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.