By linking molecular-level recognition with computational signal interpretation, this review highlights the advantages and limitations of artificial intelligence-integrated biosensors for next-generation point-of-care diagnostics, continuous health monitoring, and personalized healthcare applications.
Abstract
Biosensors have become important analytical platforms that enable rapid, selective, sensitive and portable analysis for early disease diagnosis, biomarker monitoring and point-of-care diagnostic applications. Their analytical performance depends on the coordinated function of molecular recognition elements, surface chemistry, transduction mechanisms and signal-processing strategies. Nevertheless, the analysis of real biological samples remains challenging because of low target concentrations, matrix effects, interfering species, signal noise, sensor drift and device-to-device variability. Therefore, artificial intelligence and machine learning are gaining increasing importance as data-driven tools for signal preprocessing, calibration, feature extraction, pattern recognition, quantitative prediction and diagnostic decision support. These approaches are particularly valuable for interpreting complex datasets generated by electrochemical, optical, wearable and microfluidic biosensors. This review presents an overview of healthcare-oriented biosensor systems beginning with molecular recognition principles, bioreceptor design, and transduction technologies, and extending to applications in clinical diagnosis and health monitoring. It also examines the roles of supervised, unsupervised and deep learning approaches in biosensor data analysis, while critically discussing model validation, generalizability, interpretability and clinical translation. By linking molecular-level recognition with computational signal interpretation, this review highlights the advantages and limitations of artificial intelligence-integrated biosensors for next-generation point-of-care diagnostics, continuous health monitoring, and personalized healthcare applications.
Biosensors, or biotechnology-based sensors, have evolved from laboratory prototypes into key tools in healthcare diagnostics, food safety, environmental monitoring, industrial bioprocess control, and precision agriculture. They combine biological recognition elements—enzymes, antibodies, nucleic acids, aptamers, or cells—with transduction systems such as electrochemical, optical, piezoelectric, thermal, or magnetic mechanisms to detect biochemical interactions. Advances in nanotechnology, microfluidics, synthetic biology, wearable electronics, and data-driven analytics have improved biosensor performance by enabling lower detection limits, faster responses, multiplex detection, better function in complex samples, and support for point-of-care or at-home testing. Multiplex biosensors detect multiple biomarkers simultaneously using technologies like microarrays, barcoded nanoparticles, and multi-electrode arrays, which is valuable for diseases such as cancer and inflammatory disorders. Nanomaterials—including graphene, MoS₂, carbon nanotubes, metal-organic frameworks, plasmonic nanostructures, and engineered nanoparticles—enhance sensitivity by increasing surface area and improving signal transfer. Antifouling strategies such as PEGylation, zwitterionic coatings, and hydrogels maintain sensor performance in complex samples like blood, saliva, sweat, and wastewater. Wearable biosensors use flexible materials and epidermal electronics to continuously monitor analytes in sweat, tears, or interstitial fluid and connect wirelessly to smartphones through Bluetooth or NFC for real-time monitoring. Machine learning and AI improve data analysis, while CRISPR-based biosensors using Cas12 and Cas13 allow highly specific nucleic acid detection. Synthetic biology also enables programmable cell-based biosensors. In industrial biotechnology, biosensors monitor glucose, lactate, pH, dissolved oxygen, and product levels in real time to optimize process efficiency and yield. Environmental biosensors detect heavy metals, pesticides, endocrine-disrupting chemicals, and microbial contamination, often integrated with IoT networks. However, challenges remain in receptor stability, nanomaterial reproducibility, large-scale manufacturing, regulatory approval, AI transparency, and sustainable disposal of single-use sensors.
S. Verma· International Journal of Mod...· 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
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.
Jirui Wen, Jiang Wu, Yi Yang et al.· Chinese Medical Journal· 0 citations
Recent advances in biomedical sensing technologies have enabled continuous monitoring of biological processes across multiple levels, ranging from cellular imaging to physiological and clinical health indicators. However, the heterogeneous nature of these data sources presents significant challenges for effective integration and interpretation in intelligent healthcare systems. Moreover, many artificial intelligence (AI) models used in biomedical analysis operate as opaque “black-box” systems, limiting their transparency and reliability in clinical applications. This study proposes a multimodal explainable artificial intelligence framework for real-time cellular biosensing in intelligent healthcare systems. The proposed approach integrates cellular microscopy imaging, wearable physiological biosensor signals, and clinical monitoring data to capture complementary biological information across multiple sensing layers. Cellular imaging data from the Broad Bioimage Benchmark Collection (BBBC021), wearable biosensor signals from the WESAD dataset, and clinical physiological measurements from the MIMIC-IV database were used to evaluate the framework. A multimodal fusion architecture was developed to combine modality-specific feature representations, while explain ability mechanisms such as feature attribution and saliency visualization were incorporated to enhance model transparency. Findings demonstrate that the proposed multimodal model achieves improved predictive performance compared with traditional machine learning and single-modality deep learning approaches. In addition, the explain ability module provides interpretable insights into the relationships between cellular morphology, physiological biosignals, and prediction outcomes. These findings highlight the potential of explainable multimodal AI frameworks to support trustworthy and transparent biomedical sensing systems for next-generation intelligent healthcare applications.
Xinran Wang, Ethan Lin, M. Wang et al.· International Conference on...· 0 citations
Functionalized design is presented as an application‐backward, cross‐scale framework that links clinical needs and biomarker–matrix constraints with recognition chemistry, biointerfaces, functional materials, transduction architectures, calibration, data interpretation, manufacturability, and validation.
Yanan Li, Yang Zhou, Meng Yang et al.· Advanced Materials & Technol...· 0 citations
Around 20 million cancer cases were diagnosed around the world in 2022, killing almost 10 million individuals. Additionally, there are projections for this pattern to continue to rise significantly in years ahead. The methods typically used to provide cancer diagnoses (i.e. imaging, histopathology, centralized immunoassays) have limitations in terms of sensitivity at early stages, high cost, infrastructure requirements, and delays in obtaining timely diagnoses that prevent real-time and ongoing monitoring of disease progression. Biosensors fill these voids by enabling sensitive, specific, and rapid identification of circulating cancer biomarker from low-invasiveness liquid biopsies. As such, biosensors create new opportunities for early diagnosis of cancer and ongoing monitoring. This review summarizes relevant biosensor types, including electrochemical, optical, mass-sensitive, microfluidic, nano-biosensors (recently developed), and hydrogel-based systems with discussion of their underlying analytical principles and performance for clinically important cancer biomarkers. Recent advancements in nanomaterials, micro-engineering, and the processing of data have significantly improved the sensitivity of biosensors and their ability to perform multiplex analysis within oncology at point-of-care locations. Some of the remaining barriers to progress include, matrix effect, standardization for all types of biosensors, large-scale manufacturing capabilities, regulatory approval processes, and overall clinical translation. Areas of future research focus on biosesnsors capable of multiplex, multi-modal use AI-assisted platforms that can be integrated into precision cancer treatment.