A review of recent progress in AI-assisted biosensor development highlights how AI can link molecular design, device engineering, and signal interpretation to accelerate the development of next-generation biosensors.
Abstract
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.
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
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
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.
Malaria remains one of the most significant infectious diseases worldwide, requiring rapid, sensitive, and accessible diagnostic tools to improve disease management and control. Conventional diagnostic methods, including microscopy, rapid diagnostic tests, and nucleic acid amplification techniques, present limitations in sensitivity, specificity, cost, or field applicability. This review examines the emerging role of aptamer-based biosensors (aptasensors) as innovative alternatives for malaria detection. Aptamers are synthetic nucleic acid ligands that offer high affinity and specificity toward malaria biomarkers while providing advantages over antibodies, including improved stability, lower production costs, and ease of chemical modification. The review discusses aptamer selection methodologies, major Plasmodium biomarkers targeted for detection, and the integration of aptamers into electrochemical, optical, magnetic, and microfluidic biosensing platforms. Current advances demonstrate the potential of aptasensors to enable highly sensitive, selective, and portable point-of-care diagnostics for malaria surveillance and management.
J. Centelles, Santiago Imperial· Biosensors· 0 citations
Nanozymes, a class of nanomaterials with intrinsic enzyme‐like catalytic activities, have emerged as promising platforms for catalysis‐driven diagnostics, therapeutics, and environmental sensing. Yet the multiscale structural heterogeneity of nanozymes and the nonlinear coupling between physicochemical descriptors and catalytic performance have hindered the establishment of predictive structure‐activity relationships. The rapid integration of artificial intelligence, particularly machine learning and deep learning, is reshaping this landscape by enabling systematic data integration, descriptor engineering, quantitative performance prediction, and mechanism‐informed optimization. This Review summarizes computational and data‐driven paradigms for rational nanozyme engineering, encompassing cross‐source data infrastructures, physicochemical feature representation, model architectures, and closed‐loop experimental validation. Representative advances in AI‐guided nanozyme design and high‐throughput computational screening are highlighted, alongside emerging applications in intelligent diagnostics, precision therapeutics, and environmental surveillance. By bridging materials science, catalysis theory, and data intelligence, AI‐enabled strategies provide a coherent framework for accelerating nanozyme discovery and functional optimization, paving the way toward more precise and scalable catalytic nanomaterials.
Xiaolin Guo, Hao Zhang, Zihan Zhao et al.· Journal of Intelligent Medic...· 0 citations
Rapid and sensitive detection of disease‐related biomarkers is critical for early diagnosis and appropriate treatment. However, conventional analytical methods are often time‐consuming and require complex procedures and skilled personnel. Electrochemical biosensors have therefore attracted considerable attention because they offer high sensitivity, rapid response, low cost and strong potential for point‐of‐care diagnostics. Recent efforts have focused on enhancing analytical performance through electrode interface engineering and signal amplification strategies. In particular, nanomaterials are widely employed to improve interfacial signal transduction and increase the loading density of biorecognition elements. In addition, CRISPR/Cas systems provide highly specific target recognition and inherent signal amplification, enabling the sensitive detection of diverse analytes. More recently, microfluidic technologies have emerged as key components in electrochemical biosensing platforms, enabling precise manipulation of small sample volumes, reduced reagent consumption, accelerated reaction kinetics and automated sample‐to‐answer analysis. They also facilitate multiplexed detection and support the development of compact, portable diagnostic devices. Furthermore, microfluidic platforms enable the integration of multiple analytical steps, including sample preparation, target recognition and signal readout, within a single miniaturised system, thereby improving analytical efficiency while minimising user intervention and operational errors. These advantages make microfluidic‐integrated electrochemical biosensors promising candidates for next‐generation portable diagnostic technologies. In this review, we summarise the fundamental principles of electrochemical biosensors and highlight recent advances in nanomaterial‐based interface design, CRISPR‐assisted electrochemical sensing and microfluidic‐integrated platforms for practical diagnostic applications.
M. Ahn, Sang Baek Sim, Izzati Haizan et al.· Electrochemical Science Adva...· 0 citations