Foundation Model–Driven Multimodal Health Retrieval: Integrating PPG Physiological Signals and Medical Imaging via Scalable Deep Hashing - Aarav Chopra; Sunil J. Gandhi; Yuetang Pan (International Journal of Clinical and Translational Medicine, 2026)
The rapid proliferation of wearable sensors and medical imaging has created unprecedented volumes of heterogeneous health data, motivating the development of intelligent retrieval systems that can exploit both continuous physiological signals and visual diagnostic evidence. This paper presents a system-level investigation of foundation model–driven multimodal health retrieval that integrates photoplethysmogram (PPG) signals and medical imaging through scalable deep hashing. We argue that the confluence of self-supervised foundation models pre-trained on diverse physiological and imaging corpora, coupled with advanced asymmetric deep hashing techniques, offers a transformative pathway toward cross-modal semantic search in clinical environments. Departing from task-specific model training, the proposed architecture leverages modality-specific foundation models to produce unimodal embeddings that are then aligned in a shared latent space and encoded into compact binary hash codes via margin-scalable, self-supervised hashing mechanisms. The paper examines critical system dimensions including modular design, infrastructure requirements, computational sustainability, robustness against distributional shifts, fairness across demographic groups, and governance frameworks that reconcile performance with patient privacy and regulatory compliance. By synthesizing insights from medical AI, hashing theory, and socio-technical systems research, we delineate the structural trade-offs inherent in building a production-grade multimodal retrieval system for health analytics. The discussion further elaborates on deployment strategies that span edge–cloud continuua, the necessity of continuous model monitoring, and the policy implications of embedding such retrieval engines within clinical decision support pipelines. The paper advances a forward-looking perspective on how foundation model–driven hashing can unlock new classes of health applications while remaining attentive to the ethical and operational complexities of large-scale healthcare AI.