An integrated biosensing framework that treats readout reliability as an explicit engineering objective rather than a post hoc correction problem, and establishes a generalizable strategy for constructing trustworthy POCT systems in which chemical signal generation and digital interpretation are co-designed.
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.
Özge Altıntaş, Adil Denizli· Electronics· 0 citations
The coronavirus disease 2019 (COVID-19) pandemic highlighted the need for rapid, accurate, and point-of-care diagnostic technologies, accelerating interest in biosensors as next-generation analytical platforms. However, biosensor performance is governed by a connected sequence of processes, including bioprobe–target recognition, sensor fabrication, structural optimization, and signal interpretation. Because these processes involve multiple interacting variables, conventional empirical approaches often have limitations in efficiently optimizing biosensor performance and interpreting complex analytical signals. Artificial intelligence (AI) and machine learning (ML) provide tools to model these relationships and support prediction-guided biosensor development. This review discusses recent progress in AI-assisted biosensor development in three sequential stages. First, AI-assisted bioprobe design is reviewed, including in silico aptamer discovery, smart-SELEX-based aptamer screening, and peptide receptor design for improving molecular recognition. Second, AI-driven sensor fabrication and structural optimization are discussed, focusing on electrochemical feature extraction, paper-based microfluidic device optimization, and optical biosensor parameter prediction. Third, ML-based signal analysis is examined as a strategy for converting complex electrochemical, colorimetric, and optical responses into quantitative analytical outputs. By organizing these examples as a connected workflow rather than as separate applications, this review highlights how AI can link molecular design, device engineering, and signal interpretation to accelerate the development of next-generation biosensors.
Yunseon Han, Haebin Jo, Minyoung Ju et al.· Biosensors· 0 citations
Electrochemical biosensing technologies offer an important route toward continuous and body‐interfaced health monitoring, but their translational value cannot be judged by analytical sensitivity, miniaturization, or device integration alone. This Review presents functionalized design 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. We first define this framework and examine platform‐level opportunities and failure modes across wearable, minimally invasive transdermal, implantable, and complementary transistor‐based systems. We then discuss how biomarker class, matrix accessibility, temporal dynamics, and clinical role determine sensing requirements, before evaluating representative technologies across four task‐defined scenarios: longitudinal monitoring, early molecular detection and risk stratification, continuous sensing in difficult physiological environments, and diagnosis‐linked wound management. Finally, we assess data intelligence and translation with emphasis on data quality, model robustness, interpretability, technology readiness, manufacturing reproducibility, clinical utility, regulation, and deployment. By distinguishing proof‐of‐concept performance from evidence relevant to practical use, this review provides a design and assessment framework for advancing electrochemical biosensing toward reliable, clinically actionable healthcare systems.
Yanan Li, Yang Zhou, Meng Yang et al.· Advanced Materials & Technol...· 0 citations
Pathogenic bacterial infections remain a persistent global public health crisis. However, traditional clinical detection methods—such as culture-based assays and polymerase chain reaction (PCR)—are often time-consuming and labor-intensive, and they lack sufficient sensitivity for low-abundance pathogens, hindering rapid point-of-care diagnosis. With label-free, ultra-sensitive molecular fingerprinting, Surface-Enhanced Raman Scattering (SERS) has emerged as a powerful tool for rapid pathogen identification. This review summarizes the evolution of SERS-based bacterial detection from fundamental nanomaterials to integrated clinical diagnostic platforms. The article explores four core dimensions: functional integration and enrichment strategies of colloidal probes; structural design and multifaceted capture mechanisms of solid substrates; synergistic advantages of microfluidic systems in enabling automated “sample-to-answer” architectures; and the translational potential of SERS-based lateral flow assays (LFAs) for robust point-of-care testing (POCT). This review reveals a research shift from maximizing electromagnetic enhancement toward overcoming matrix effects in clinical samples and ensuring robustness. In synergy with microfluidics, LFAs, and AI, SERS technology is bridging the bench-to-bedside gap, offering a roadmap for next-generation decentralized, high-precision diagnostics.
Yueqi Yang, Jing Li, Xinyi Hu et al.· Biosensors· 0 citations
Blood-based biomarkers have great potential for the early diagnosis of Alzheimer’s disease (AD); however, their implementation in primary healthcare screening remains largely limited owing to their reliance on sophisticated instruments and complex procedures. Furthermore, the complexity of the biological matrix poses a significant challenge to detection without washing and the use of reagents. Herein, we report a single-pot electrochemical sensing platform that enables rapid and ultrasensitive quantitative detection of AD biomarkers directly from untreated blood or plasma within 20 min of collection, without sample preparation, washing, or using additional reagents. This platform integrated high-affinity antibodies with an electric field-driven sensing mechanism based on a molecular pendulum. Upon the application of a potential, a negatively charged DNA scaffold moved directionally toward the electrode, enabling the real-time dynamic detection of surface-confined redox reporters and precise biomarker quantification. We validated the platform in 80 clinical plasma samples using Aβ42 and p-tau217 as representative biomarkers. We found that combined biomarker analysis outperformed single-analyte detection and achieved an area under the curve of 0.9475, with 82.5% sensitivity and 92.5% specificity. This work establishes a truly wash-free and reagentless electrochemical sensing strategy, showing great potential for early diagnosis of AD in community or home settings.
Xuewei Du, Taoping Zhang, Zhongzhong Wan et al.· ACS Sensors· 0 citations
Accurate diagnosis of biomarkers of breast cancer (BC) at an early stage is at the centre of the reduction in breast cancer (BC) mortality. In order to provide an alternative, low cost, compact and fast measurement methods to traditional immunoassays and imaging techniques, electrochemical biosensors are now being developed, controlling the analytical performance of the biosensors by choosing the appropriate biorecognition element. This review highlights the comparison of two electrochemical platforms—antibody-based platform (immunosensor) and aptamer-based platform (aptasensor)—developed from 2021 to 2026 for four important BC biomarkers, human epidermal growth factor receptor 2 (HER2), carbohydrate antigen 15-3 (CA 15-3), carcinoembryonic antigen (CEA), and circulating microRNA-21. The limits of detection (LOD), linear range, stability and reproducibility data reported in the literature were reviewed and combined quantitatively, and presented with qualitative consensus scoring for production cost, batch reproducibility and clinical maturity. In general, aptamer-based platforms have an advantage in terms of their chemical stability and low batch-to-batch variability, as antibody-based platforms have an advantage in terms of their analytical maturity and availability, and the analytical level appears to be more dependent on the nanomaterial signal- amplification strategy than on only the recognition-element class. The heterogeneous nature of the validation methods used in different studies makes statistical comparison of studies difficult, and is one of the main translational barriers.
Manasa T L, Rajeshkumar Upadhyaya, D. H B· International Journal For Mu...· 0 citations
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
