Advances in Bio-Integrated Edge Computing Systems for Smart and Connected Health

Recent advances in bio-integrated healthcare technologies have enabled continuous, high-resolution monitoring and active therapeutic intervention through wearable, implantable, and ingestible platforms. In parallel, edge computing has emerged as a critical framework for processing biomedical data locally, enabling low-latency, energy-efficient, and privacy-preserving operation. This review provides a comprehensive overview of bio-integrated edge computing systems for smart and connected health, focusing on the co-design of computing, sensing, and energy strategies. We first examine computing approaches, including conventional von Neumann architectures and emerging paradigms such as compute-in-memory and neuromorphic systems, for on-device data processing and decision-making. We then discuss sensing strategies that enhance signal fidelity and enable efficient information representation at the biointerface. Finally, we review energy solutions that enable continuous, autonomous operation. By integrating these domains, this work outlines pathways toward scalable, adaptive, and fully autonomous edge healthcare systems.