Recent advances in smart nanosensors and IoT-based systems for real-time diagnostic monitoring.

Abstract

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.