Self-Supervised Physiological Foundation Models for Early Risk Prediction and Secure Human–AI Collaboration in Healthcare - Henrik D. Bush; Bjay Srinivasan (Bioinformatics Insights and Analytics, 2026)
The transformation of physiological monitoring from episodic clinical encounters to continuous, multimodal data streams opens unprecedented opportunities for early risk prediction and personalized intervention. Self-supervised physiological foundation models, pretrained on vast corpora of electrocardiogram, photoplethysmogram, electroencephalogram, and related biosignals, are poised to become the backbone of next-generation health intelligence systems. This paper presents a system-level analysis of the architectural paradigms, deployment architectures, and governance frameworks necessary to translate such models into trustworthy, secure human–AI collaborations. We examine how contrastive, generative, and masked-reconstruction objectives can capture latent physiological dynamics without costly manual annotations, enabling early detection of cardiovascular, neurological, and metabolic perturbations months before clinical manifestation. The discussion extends beyond model accuracy to encompass the full sociotechnical stack: federated and split learning infrastructures that preserve privacy, edge-cloud orchestration for real-time inference, adversarial resilience against data poisoning and evasion attacks, and continuous monitoring of fairness and calibration drift across heterogeneous populations. Special attention is devoted to the integration of large language model agents in clinical decision pipelines, where secure human-AI collaboration requires adversarial robustness guarantees and transparent uncertainty communication. Through cross-domain comparisons with imaging and natural language foundation models and forward-looking policy analysis, we argue that sustainable, equitable deployment demands institutional mechanisms for auditability, dynamic consent, and algorithmic stewardship. The paper concludes by outlining a research roadmap that couples self-supervised representation learning with system-level safeguards to achieve early risk prediction without compromising patient autonomy or clinical safety.