Adversarially Robust Multimodal Medical Agents Integrating PPG Foundation Models for Secure Clinical Decision Support - Roben Rliver; Niklas C. Hawkins (International Journal of Clinical and Translational Medicine, 2026)
The accelerating integration of artificial intelligence into clinical workflows demands architectures that not only improve diagnostic precision but also guarantee operational security under adversarial conditions. This paper presents a comprehensive systems-level analysis of adversarially robust multimodal medical agents that incorporate photoplethysmography (PPG) foundation models for secure clinical decision support. By combining large language model reasoning with continuous, self-supervised representations of cardiovascular dynamics, these agents offer a pathway toward contextually aware, real-time patient monitoring. However, the multimodal fusion surface expands the attack vectors available to adversaries, including evasion perturbations on physiological signals, prompt injection into clinical dialogue, and data poisoning targeting pretraining pipelines. We examine the structural trade-offs inherent in designing such agents, focusing on the interaction between the PPG foundation model’s inductive biases, the orchestration layer’s adversarial hardening, and the governance frameworks required for clinical deployment. A layered defense architecture is proposed, integrating statistical-prior-informed robust representation learning, certified input filtering, and runtime cross-modal verification. System-level evaluation criteria are discussed in terms of clinical utility, robustness-accuracy trade-offs, latency constraints, and equity across diverse patient populations. The work further elaborates on regulatory alignment, liability distribution, and the long-term sustainability of secure medical AI ecosystems. By bridging foundational physiological modeling with adversarial machine learning, the paper contributes a blueprint for next-generation clinical agents that are both intelligent and resilient.