Intelligent medical sensors for smart healthcare and precision medicine - Jirui Wen; Jiang Wu; Yi Yang; Ling Wang; Fan Zhang (Chinese Medical Journal, 2026)
Abstract Conventional medical sensors primarily function as passive transducers and often struggle to maintain accuracy and stability under complex physiological conditions. Their limited capacity for local data processing, adaptive compensation, and real-time interaction also constrains continuous monitoring and individualized clinical decision-making. The convergence of microelectronics, materials science, wireless communication, and artificial intelligence (AI) has therefore accelerated the development of intelligent medical sensors that integrate sensing, processing, communication, and decision-support functions. This review summarizes recent advances and remaining challenges in this field. We examine the technical architectures and core functions of next-generation sensors, including self-compensation, self-calibration, self-diagnosis, and bidirectional data interaction, and compare four major technological platforms: flexible wearable sensors, optical fiber sensors, electrochemical sensors, and functional nucleic acid and molecularly imprinted biosensors. We further discuss signal-transduction, anti-interference, and data-transmission mechanisms; advances in flexible materials, micro/nano-fabrication, multimodal integration, and AI-enabled signal processing; and applications in physiological monitoring, biomarker detection, chronic disease management, wearable therapy, interventional support, and extreme environments. Despite rapid progress, clinical translation remains limited by data security and privacy risks, insufficient standardization and regulatory alignment, long-term stability and biocompatibility concerns, and uneven validation maturity across technologies. Future development should prioritize clinically driven design, staged and technology-specific validation, multimodal and low-power integration, and coordinated regulatory and manufacturing strategies to support reliable, scalable, and patient-centered implementation.