Aug 2026· Chinese Medical Journal· 0 citations· 171 references
Medicine
TL;DR
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.
Abstract
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.
The integration of smart nanosensor systems with artificial intelligence (AI) and the Internet of Things (IoT) is transforming diagnostic monitoring in biomedical, environmental, and industrial applications. These advancements facilitate real-time data acquisition, continuous physiological tracking, and point-of-care diagnostics, significantly enhancing early disease detection and personalized care. Innovations in nanomaterials, such as graphene, quantum dots, and plasmonic nanoparticles, have bolstered sensing capabilities, allowing for high sensitivity, rapid responses, and the selective detection of important biomarkers. However, challenges remain in translating these nanosensor technologies from the lab to real-world applications, including reliability, stability, integration with digital platforms, data security, power supply for ongoing monitoring, and clinical biocompatibility. Frequently, current systems operate in silos, lacking coordination between sensing, data processing, and decision-making components. This study proposes a systems integration framework that unifies smart nanosensors, AI-driven analytics, and the IoT into a single platform for continuous health monitoring. The main findings reveal that nanosensor-enabled systems can significantly enhance diagnostic performance, with graphene-based nanosensors detecting C-reactive protein in human serum at concentrations near 27 pM, and plasmonic sensors offering swift, portable detection. Additionally, electrochemical immunosensors achieve sensitivity for oral cancer biomarkers at concentrations as low as 1.424 fg mL-1 within just 120 seconds. The novel contribution of this review is the proposed systems-integration approach that unifies smart nanosensors, AI-driven analytics, and IoT into a cohesive platform for continuous health monitoring and data-driven decision-making in healthcare, showcasing the potential for improved diagnostic efficiency and patient outcomes. By proposing a multi-layered systems integration framework, this review synthesizes current advances in smart nanosensors, AI, and IoT into a unified architecture that addresses translational challenges and supports the development of intelligent, real-time, and patient-centered healthcare systems.
B. O. Omiyale, Akinola Ogbeyemi, Jethro Odeyemi et al.· Analytical Methods· 0 citations
This review of sensing and biosensing technologies for health-related applications is expected to provide an overview of the field, identify promising research directions, and inspire future developments.
L. Brazaca, Alvaro Moreno Lozano, Beatriz Mayol et al.· ACS Applied Materials and In...· 1 citation
This work connects biomedical signal capture, on-device AI, clinical translation, and digital ethics to delineate a roadmap for frontier AI wearables, and proposed edge-aware transformer-based models, privacy-preserving analytics, and global multimodal sensing advance intelligent, scalable, and fair AI-driven health monitoring systems.
Functional materials-based chemical sensors play a crucial role in industrial process control, environmental monitoring, medical diagnostics, and safety assurance. Nearly all conventional chemical sensors rely on static material properties and specific operating parameters despite substantial advances in sensing materials and device fabrication, prompting minimal adaptability under inconsistent and complex environments. On the grounds of these constraints, there has been growing interest in developing intelligent chemical sensing, where adaptive behaviour is employed to improve selectivity, robustness, and prolonged stability. This review envisages intelligent chemical sensors as learning-enabled platforms for adaptive chemical detection while propounding a materials-centric yet system-aware vantage on intelligent chemical sensors. The integration of intelligence through the sensing pipeline, adaptive transduction strategies, encompassing hybrid materials and responsive ceramics, learning paradigms, and incorporated sensing architectures is discussed here. Functional materials are intended to enable selectivity, plasticity, drift mitigation, and dynamic sensitivity, while endorsed by system-level incorporation and learning-assisted signal interpretation. Reliable chemical detection in elaborate environments executed by intelligent material device system coupling is highlighted in representative examples. Key challenges associated with material stability, interpretability, data scarcity, and energy efficiency are critically examined besides emerging research directions such as memory-enabled sensing interfaces, chemically adaptive materials, and autonomous sensing ecosystems. This review intends to bridge materials innovation and intelligent system design, proposing perceptiveness for the evolution of next-generation adaptive chemical sensors.
Rashida Batool, Nazmina Imrose Sonil, M. Nazar et al.· RSC Advances· 0 citations
Smart wearable biosensors represent a significant paradigm shift from one-time sample analysis to real-time biochemical monitoring at the body interface. Besides the flexible design of the device or wireless readout, their clinical utility will also require the reliability of the entire sensing pathway under real physiological conditions. This pathway involves biofluid access to clinical interpretation. Despite rapid progress, many wearable biosensor platforms remain limited by weak biofluid–blood correlation, receptor degradation, biofouling, motion artefacts, sensor drift and insufficient patient-level validation. Thus, a chemistry-to-clinics approach is crucial to assess the analytical reliability and translational readiness of recognition elements, sensing materials, and engineered biointerfaces. Enzymes, antibodies, aptamers, nucleic-acid systems, molecularly imprinted polymers, and nanozymes are discussed within the context of selectivity, stability, antifouling behaviour and suitability for continuous monitoring of sweat, interstitial fluid, tears, wound exudate and breath condensate. The functionality of carbon nanostructures, metal-based nanomaterials, hydrogels, MXenes, metal–organic frameworks and self-powered interfaces are evaluated in terms of their applications in amplification, mechanical conformity, biofluid handling and signal stability. Artificial intelligence is positioned as a support layer for signal correction, calibration, classification, multimodal fusion and predictive interpretation, rather than as a substitute for robust sensing chemistry. This review provides a critical chemistry-to-clinical perspective on smart wearable biosensors and outlines the validation, manufacturing, cybersecurity, post-market surveillance and benchmarking requirements needed for their translation into reliable diagnostic and therapeutic-monitoring technologies.
Yachana Misha, Radheshyam Jena, Aman Shukla et al.· RSC Advances· 0 citations
This review provides a comprehensive overview of recent advances in flexible electrochemical sensors, with particular emphasis on nanomaterial engineering, device fabrication strategies, and biomedical applications, and examines current limitations.
