Research Seminar by Dr Dimitris Spathis | The era of foundation models – AI for personal health as its ultimate use case

The era of foundation models – AI for personal health as its ultimate use case

The unprecedented success of foundation models has transformed how we think about artificial intelligence, yet their application to personal health has been limited. In this talk, I will share my journey in building AI for health monitoring. I’ll begin by highlighting our recent works to improve data efficiency and robustness through self-supervision, by leveraging the non-stationarity of biosignals, reducing catastrophic forgetting, enhancing fairness through pre-training, and learning from multimodal biosignals. The main focus will be on our latest project: PaPaGei, the first open foundation model for biosignals. Ever noticed the green light under your smartwatch or the red light on your finger oximeter at the hospital? These ubiquitous sensors use photoplethysmography (PPG) – a fundamental technique for monitoring cardiovascular health, sleep, and overall wellbeing. While domains like text and audio have been revolutionized by foundation models such as BERT, Whisper, and Llama, other data types like biosignals have lagged behind due to data constraints and privacy concerns. PaPaGei changes this paradigm by providing a robust, pre-trained model that researchers and developers can freely build upon. Pre-trained on over 57,000 hours of publicly available PPG data, it demonstrates improvements across 20 health tasks, from sleep monitoring to pregnancy assessment. I will share how this project represents a milestone in a journey that began five years ago with Step2Heart, where we first demonstrated the potential of pre-trained models for health monitoring. Drawing from both academic and industrial perspectives, I will discuss how foundation models like PaPaGei can accelerate progress in health AI while addressing critical challenges in generalization.

About the Speaker