Role of Machine Learning Assisted Biosensors in Point-of-Care-Testing For Clinical Decisions

Artificial intelligence (AI) in point-of-care testing

The integration of artificial intelligence (AI) into point-of-care testing (POCT) represents a transformative leap in modern healthcare, addressing critical challenges in diagnostic accuracy, workflow efficiency, and equitable access. While POCT has revolutionized decentralized care through rapid results, its potential is hindered by variability in accuracy, integration hurdles, and resource constraints. AI technologies-encompassing machine learning, deep learning, and natural language processing-offer robust solutions: convolutional neural networks improve malaria detection in sub-Saharan Africa to 95 % sensitivity, while predictive analytics reduce device downtime by 20 % in resource-limited settings. AI-driven decision support systems curtail antibiotic misuse by 40 % through real-time data synthesis, and portable AI devices enable anaemia screening in rural India with 94 % accuracy, slashing diagnostic delays from weeks to hours. Despite these advancements, challenges persist, including data privacy risks, algorithmic opacity, and infrastructural gaps in low- and middle-income countries. Explainable AI frameworks and blockchain encryption are critical to building clinician trust and ensuring regulatory compliance. Future directions emphasize the convergence of AI with Internet of Things (IoT) and blockchain for predictive diagnostics, as demonstrated by AI-IoT systems forecasting dengue outbreaks 14 days in advance. Personalized medicine, powered by genomic and wearable data integration, further underscores AI potential to tailor therapies, reducing cardiovascular events by 25 %. Realizing this vision demands interdisciplinary collaboration, ethical governance, and equitable implementation to bridge global health disparities. By harmonizing innovation with accessibility, AI-enhanced POCT emerges as a cornerstone of proactive, patient-centered healthcare, poised to democratize diagnostics and drive sustainable health equity worldwide.

Bioelectroanalytical Technologies for Advancing the Frontiers To Democratize Personalized Desired Health

The breakthroughs experienced in the development of cutting-edge, reliable, and multipurpose (bio)electroanalytical technologies and their successful incursion into underexplored scenarios have demonstrated their unique potential to act as enablers of the ongoing transformation from reactive to predictive, preventive, personalized, and participatory healthcare, currently known as "P4" medicine. This transformation, more than a vision, is a necessity to achieve a new generation of more efficient, sustainable, and tailored to individual needs healthcare. This promising outlook is the focus of this Perspective, which in addition to highlighting some of the most shocking related research over the last 5 years, offers a prospective and insightful view of the opportunities and imminent advances that shape the future of these fascinating technologies.

Paper-Based Vertical Flow Assays for in Vitro Diagnostics and Environmental Monitoring

Microfluidic paper-based analytical devices (μPADs) are powerful tools for diagnostic and environmental monitoring. Being affordable and portable, μPADs enable rapid detection of small molecules, heavy metals, and biomolecules, thereby decentralizing diagnostics and expanding biosensor accessibility. However, the reliance on two-dimensional fluid flow restricts the utility of conventional μPADs, presenting challenges for applications that require simultaneous multibiomarker analysis from a single sample. Vertical flow paper-based analytical devices (VF-μPADs) overcome this challenge by allowing axial fluid movement through paper stacks, offering several advantages, including (1) enhanced multiplexing capabilities, (2) reduced hook effect for improved accuracy, and (3) shorter assay times. This review provides an overview of VF-μPADs technologies, exploring structural and functional performance trade-offs between VF-μPADs and conventional lateral flow systems. The sensing performance, fabrication methods, and applications in in vitro diagnostics and environmental monitoring are discussed. Furthermore, critical challenges─such as fabrication complexity, data analysis, and scalability─are addressed, along with proposed strategies for mitigating these barriers to facilitate broader adoption. By examining these strengths and challenges, this review presents the potential of VF-μPADs to advance point-of-care testing, particularly in resource-limited settings.

Integrated Photonic Biosensors: Enabling Next-Generation Lab-on-a-Chip Platforms

Integrated photonic biosensors are revolutionizing lab-on-a-chip technologies by providing highly sensitive, miniaturized, and label-free detection solutions for a wide range of biological and chemical targets. This review explores the foundational principles behind their operation, including the use of resonant photonic structures such as microring and whispering gallery mode resonators, as well as interferometric and photonic crystal-based designs. Special focus is given to the design strategies that optimize light-matter interaction, enhance sensitivity, and enable multiplexed detection. We detail state-of-the-art fabrication approaches compatible with complementary metal-oxide-semiconductor processes, including the use of silicon, silicon nitride, and hybrid material platforms, which facilitate scalable production and seamless integration with microfluidic systems. Recent advancements are highlighted, including the implementation of optofluidic photonic crystal cavities, cascaded microring arrays with subwavelength gratings, and on-chip detector arrays capable of parallel biosensing. These innovations have achieved exceptional performance, with detection limits reaching the parts-per-billion level and real-time operation across various applications such as clinical diagnostics, environmental surveillance, and food quality assessment. Although challenges persist in handling complex biological samples and achieving consistent large-scale fabrication, the emergence of novel materials, advanced nanofabrication methods, and artificial intelligence-driven data analysis is accelerating the development of next-generation photonic biosensing platforms. These technologies are poised to deliver powerful, accessible, and cost-effective diagnostic tools for practical deployment across diverse settings.

Point-of-Care Testing: The Convergence of Innovation and Accessibility in Diagnostics

Over the years, the evolution of point-of-care testing (POCT) has been driven by technological advancements in materials, design, and artificial intelligence, as well as breakthrough developments in wearable technologies. These innovations are shifting diagnostics from centralized medical facilities to individual homes, meeting the growing demand for personalized healthcare. This Review explores recent advancements in binding-based assay technologies over the past two years, focusing on platforms such as traditional flow assays (lateral and vertical flow), fully integrated microfluidic devices, and wearable biosensor-integrated systems for POCT applications. It emphasizes the role of optical and electrochemical detection methods, which are essential for ensuring the sensitivity, specificity, and reliability required in a POCT. POCT technologies offer advantages including ease of use, high diagnostic accuracy, rapid clinical assessment, and cost-effectiveness in manufacturing and consumables. Additionally, the Review highlights current challenges and future perspectives for delivering personalized healthcare through portable and wearable POCT systems that operate on a sample-in, result-out basis.

Advanced Materials for Biological Field-Effect Transistors (Bio-FETs) in Precision Healthcare and Biosensing

Biological Field Effect Transistors (Bio-FETs) are redefining the standard of biosensing by enabling label-free, real-time, and extremely sensitive detection of biomolecules. At the center of this innovation is the fundamental empowering role of advanced materials, such as graphene, molybdenum disulfide, carbon nanotubes, and silicon. These materials, when harnessed with the downstream biomolecular probes like aptamers, antibodies, and enzymes, allow Bio-FETs to offer unrivaled sensitivity and precision. This review is an exposition of how advancements in materials science have permitted Bio-FETs to detect biomarkers in extremely low concentrations, from femtomolar to attomolar levels, ensuring device stability and reliability. Specifically, the review examines how the incorporation of cutting-edge materials architectures, like flexible / stretchable and multiplexed designs, is expanding the frontiers of biosensing and contributing to the development of more adaptable and user-friendly Bio-FET platforms. A key focus is placed on the synergy of Bio-FETs with artificial intelligence (AI), the Internet of Things (IoT), and sustainable materials approaches as fast-tracking toward transition from research into practical healthcare applications. The review also explores current challenges such as material reproducibility, operational durability, and cost-effectiveness. It outlines targeted strategies to address these hurdles and facilitate scalable manufacturing. By emphasizing the transformative role played by advanced materials and their cementing position in Bio-FETs, this review positions Bio-FETs as a cornerstone technology for the future healthcare solution for precision applications. These advancements would lead to an era where material innovation would herald massive strides in biomedical diagnostics and subsume.

Smart Dust for Chemical Mapping

This review article explores the transformative potential of smart dust systems by examining how existing chemical sensing technologies can be adapted and advanced to realize their full capabilities. Smart dust, characterized by submillimeter-scale autonomous sensing platforms, offers unparalleled opportunities for real-time, spatiotemporal chemical mapping across diverse environments. This article introduces the technological advancements underpinning these systems, critically evaluates current limitations, and outlines new avenues for development. Key challenges, including multi-compound detection, system control, environmental impact, and cost, are discussed alongside potential solutions. By leveraging innovations in miniaturization, wireless communication, AI-driven data analysis, and sustainable materials, this review highlights the promise of smart dust to address critical challenges in environmental monitoring, healthcare, agriculture, and defense sectors. Through this lens, the article provides a strategic roadmap for advancing smart dust from concept to practical application, emphasizing its role in transforming the understanding and management of complex chemical systems.

Enhancing the Predictive Performance of Molecularly Imprinted Polymer-Based Electrochemical Sensors Using a Stacking Regressor Ensemble of Machine Learning Models

The performance of electrochemical sensors is influenced by various factors. To enhance the effectiveness of these sensors, it is crucial to find the right balance among these factors. Researchers and engineers continually explore innovative approaches to enhance sensitivity, selectivity, and reliability. Machine learning (ML) techniques facilitate the analysis and predictive modeling of sensor performance by establishing quantitative relationships between parameters and their effects. This work presents a case study on developing a molecularly imprinted polymer (MIP)-based sensor for detecting doxorubicin (Dox), emphasizing the use of ML-based ensemble models to improve performance and reliability. Four ML models, including Decision Tree (DT), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and K-Nearest Neighbors (KNN), are used to evaluate the effect of each parameter on prediction performance, using the SHapley Additive exPlanations (SHAP) method to determine feature importance. Based on the analysis, removing a less influential feature and introducing a new feature significantly improved the model's predictive capabilities. By applying the min-max scaling technique, it is ensured that all features contribute proportionally to the model learning process. Additionally, multiple ML models─Linear Regression (LR), KNN, DT, RF, Adaptive Boosting (AdaBoost), Gradient Boosting (GB), Support Vector Regression (SVR), XGBoost, Bagging, Partial Least Squares (PLS), and Ridge Regression─are applied to the data set and their performance in predicting the sensor output current is compared. To further enhance prediction performance, a novel ensemble model is proposed that integrates DT, RF, GB, XGBoost, and Bagging regressors, leveraging their combined strengths to offset individual weaknesses. The main benefit of this work lies in its ability to enhance MIP-based sensor performance by developing a novel stacking regressor ensemble model, which improves prediction performance and reliability. This methodology is broadly applicable to the development of other sensors with different transducers and sensing elements. Through extensive simulation results, the proposed stacking regressor ensemble model demonstrated superior predictive performance compared to individual ML models. The model achieved an R-squared (R2) of 0.993, significantly reducing the root-mean-square error (RMSE) to 0.436 and the mean absolute error (MAE) to 0.244. These improvements enhanced sensitivity and reliability of the MIP-based electrochemical sensor, demonstrating a substantial performance gain over individual ML models.

Biosensors, Artificial Intelligence Biosensors, False Results and Novel Future Perspectives

Medical biosensors have set the basis of medical diagnostics, and Artificial Intelligence (AI) has boosted diagnostics to a great extent. However, false results are evident in every method, so it is crucial to identify the reasons behind a possible false result in order to control its occurrence. This is the first critical state-of-the-art review article to discuss all the commonly used biosensor types and the reasons that can give rise to potential false results. Furthermore, AI is discussed in parallel with biosensors and their misdiagnoses, and again some reasons for possible false results are discussed. Finally, an expert opinion with further future perspectives is presented based on general expert insights, in order for some false diagnostic results of biosensors and AI biosensors to be surpassed.

Advancing clinical biochemistry: addressing gaps and driving future innovations

Modern healthcare depends fundamentally on clinical biochemistry for disease diagnosis and therapeutic guidance. The discipline encounters operational constraints, including sampling inefficiencies, precision limitations, and expansion difficulties. Recent advancements in established technologies, such as mass spectrometry and the development of high-throughput screening and point-of-care technologies, are revolutionizing the industry. Modern biosensor technology and wearable monitors facilitate continuous health tracking, Artificial Intelligence (AI)/machine learning (ML) applications enhance analytical capabilities, generating predictive insights for individualized treatment protocols. However, concerns regarding algorithmic bias, data privacy, lack of transparency in decision-making ("black box" models), and over-reliance on automated systems pose significant challenges that must be addressed for responsible AI integration. However, significant limitations remain-substantial implementation expenses, system incompatibility issues, and information security vulnerabilities intersect with ethical considerations regarding algorithmic fairness and protected health information. Addressing these challenges demands coordinated efforts between clinicians, scientists, and technical specialists. This review discusses current challenges in clinical biochemistry, explicitly addressing the limitations of reference intervals and barriers to implementing innovative biomarkers in medical settings. The discussion evaluates how advanced technologies and multidisciplinary collaboration can overcome these constraints while identifying research priorities to enhance diagnostic precision and accessibility for better healthcare delivery.

Machine learning in point-of-care testing: innovations, challenges, and opportunities

The landscape of diagnostic testing is undergoing a significant transformation, driven by the integration of artificial intelligence (AI) and machine learning (ML) into decentralized, rapid, and accessible sensor platforms for point-of-care testing (POCT). The COVID-19 pandemic has accelerated the shift from centralized laboratory testing but also catalyzed the development of next-generation POCT platforms that leverage ML to enhance the accuracy, sensitivity, and overall efficiency of point-of-care sensors. This Perspective explores how ML is being embedded into various POCT modalities, including lateral flow assays, vertical flow assays, nucleic acid amplification tests, and imaging-based sensors, illustrating their impact through different applications. We also discuss several challenges, such as regulatory hurdles, reliability, and privacy concerns, that must be overcome for the widespread adoption of ML-enhanced POCT in clinical settings and provide a comprehensive overview of the current state of ML-driven POCT technologies, highlighting their potential impact in the future of healthcare.

FEN1-Aided RPA (FARPA) Coupled with Autosampling Microfluidic Chip Enables Highly Multiplexed On-Site Pathogen Screening

A simple, rapid, low-cost, and multiplex detection platform is crucial for the diagnosis of infectious diseases, especially for on-site pathogen screening. However, current methods are difficult to satisfy the requirements for minimal instrument and multiplexed point-of-care testing (POCT). Herein, we propose a versatile and easy-to-use platform (FARPA-chip) by combining multiplex FARPA with an autosampling microfluidic chip. A pair of universal recombinase polymerase amplification (RPA) primers introduced during double-stranded cDNA (ds-cDNA) preparation are employed to amplify multiple targets, followed by amplicon-decoding with the chip, indicating no bias in amplifying different targets due to the universal RPA primers. FARPA-chip exhibits that as low as 10 copies of each target RNA in the starting sample can be sensitively detected by 12-plex detection of vector-borne viruses within 45 min and no cross-talk is observed between different targets. The feasibility of this platform is confirmed by designing a 9-plex FARPA-chip to detect 6 kinds of clinically common respiratory viruses from 16 clinical samples of nasopharyngeal swabs, and the results are completely consistent with RT-qPCR. Furthermore, by integrating quick extraction reagent, the turnaround time can be significantly decreased to <50 min, highlighting that our FARPA-chip enables a cost-effective on-site pathogen screening with a relatively high level of multiplexing. Depending on the number of chambers in the chip, the current design is theoretically capable of detecting up to 24 different pathogens, which should fulfill most clinical purposes. We believe that the proposed platform could provide an effective way for a series of healthcare-related applications in resource-limited settings.

Ultradense Electrochemical Chip and Machine Learning for High-Throughput, Accurate Anticancer Drug Screening

Despite the potentialities of electrochemical sensors, these devices still encounter challenges in devising high-throughput and accurate drug susceptibility testing. The lack of platforms for providing these analyses over the preclinical trials of drug candidates remains a significant barrier to developing medicines. In this way, ultradense electrochemical chips are combined with machine learning (ML) to enable high-throughput, user-friendly, and accurate determination of the viability of 2D tumor cells (breast and colorectal) aiming at drug susceptibility assays. The effect of doxorubicin (anticancer drug model) was assessed through cell detachment electrochemical assays by interrogating Ru(NH3)63+ with square wave voltammetry (SWV). This positive probe is presumed to imply sensitive monitoring of the on-sensor cellular death because of its electrostatic preconcentration in the so-called nanogap zone between the electrode surface and adherent cells. High-throughput assays were obtained by merging fast individual SWV measurements (9 s) with the ability of chips to yield analyses of Ru(NH3)63+ in series. The approach's applicability was demonstrated across two analysis formats, drop-casting and microfluidic assays. One should also mention that fitting a multivariate descriptor from selected input data via ML proved to be essential to providing accurate determinations (98 to 104%) of cell viability and half-maximal lethal concentration of the drug. The achieved results underscore the potential of the method in steering electrochemical sensors toward enabling high-throughput drug screening in practical applications.

Intelligent Microfluidics for Plasma Separation: Integrating Computational Fluid Dynamics and Machine Learning for Optimized Microchannel Design

Efficient separation of blood plasma and Packed Cell Volume (PCV) is vital for rapid blood sensing and early disease detection, especially in point-of-care and resource-limited environments. Conventional centrifugation methods for separation are resource-intensive, time-consuming, and off-chip, necessitating innovative alternatives. This study introduces "Intelligent Microfluidics", an ML-integrated microfluidic platform designed to optimize plasma separation through computational fluid dynamics (CFD) simulations. The trifurcation microchannel, modeled using COMSOL Multiphysics, achieved plasma yields of 90-95% across varying inflow velocities (0.0001-0.05 m/s). The input fluid parameters mimic the blood viscosity and density used with appropriate boundary conditions and fluid dynamics to optimize the designed microchannels. Eight supervised ML algorithms, including Artificial Neural Networks (ANN) and k-Nearest Neighbors (KNN), were employed to predict key performance parameters, with ANN achieving the highest predictive accuracy (R2 = 0.97). Unlike traditional methods, this platform demonstrates scalability, portability, and rapid diagnostic potential, revolutionizing clinical workflows by enabling efficient plasma separation for real-time, point-of-care diagnostics. By incorporating a detailed comparative analysis with previous studies, including computational efficiency, our work underscores the superior performance of ML-enhanced microfluidic systems. The platform's robust and adaptable design is particularly promising for healthcare applications in remote or resource-constrained settings where rapid and reliable diagnostic tools are urgently needed. This novel approach establishes a foundation for developing next-generation, portable diagnostic technologies tailored to clinical demands.

From Conventional Detection to Point-of-care Tests (POCT) Method for Pediatric Respiratory Infections Diagnosis: A Systematic Review

Bacterial respiratory infections pose significant health risks to children, particularly infants susceptible to upper respiratory tract infections (URTIs). The COVID-19 pandemic has further exacerbated the prevalence of these infections, with pathogens such as Mycoplasma pneumoniae, Streptococcus pneumoniae, Legionella pneumophila, Staphylococcus aureus, Haemophilus influenzae, and Klebsiella species commonly implicated in pediatric cases. The critical need for accurate and timely detection of these bacterial agents has highlighted the importance of advanced diagnostic techniques, including multiplex real-time PCR, in clinical practice. Multiplex real-time polymerase chain reaction (PCR) offers several advantages, including rapid results, high sensitivity, and specificity. By accelerating the diagnostic process, this approach enables early intervention and targeted treatment, ultimately improving patient outcomes. In addition to PCR technologies, rapid and point-of-care testing (POCT) play a crucial role in the prompt diagnosis of bacterial respiratory infections. These tests are designed to be user-friendly, sensitive, and deliver quick results, making them particularly valuable in urgent clinical settings. POCT tests are often categorized into two main groups: those aimed at determining the cause of infection and those focused on confirming the presence of specific pathogens. By utilizing POCT, healthcare providers can make rapid and informed treatment decisions, leading to more effective management of bacterial respiratory infections in children. As the medical community continues to explore innovative diagnostic approaches, the integration of molecular and rapid testing methods offers significant promise in the realm of bacterial respiratory infections. By adopting these cutting-edge technologies, healthcare professionals can enhance their ability to accurately diagnose these infections, tailor treatment strategies, and ultimately improve patient care.

Innovative Diagnostic Approaches and Challenges in the Management of HIV: Bridging Basic Science and Clinical Practice

Human Immunodeficiency Virus (HIV) remains a major public health challenge globally. Recent innovations in diagnostic technology have opened new pathways for early detection, ongoing monitoring, and more individualized patient care, yet significant barriers persist in translating these advancements into clinical settings. This review highlights the cutting-edge diagnostic methods emerging from basic science research, including molecular assays, biosensors, and next-generation sequencing, and discusses the practical and logistical challenges involved in their implementation. By analyzing current trends in diagnostic techniques and management strategies, we identify critical gaps and propose integrative approaches to bridge the divide between laboratory innovation and effective clinical application. This work emphasizes the need for comprehensive education, supportive infrastructure, and multi-disciplinary collaborations to enhance the utility of these diagnostic innovations in improving outcomes in patients with HIV.

Deep Learning-Enhanced Portable Chemiluminescence Biosensor: 3D-Printed, Smartphone-Integrated Platform for Glucose Detection

A novel, portable chemiluminescence (CL) sensing platform powered by deep learning and smartphone integration has been developed for cost-effective and selective glucose detection. This platform features low-cost, wax-printed micro-pads (WPµ-pads) on paper-based substrates used to construct a miniaturized CL sensor. A 3D-printed black box serves as a compact WPµ-pad sensing chamber, replacing traditional bulky equipment, such as charge coupled device (CCD) cameras and optical sensors. Smartphone integration enables a seamless and user-friendly diagnostic experience, making this platform highly suitable for point-of-care (PoC) applications. Deep learning models significantly enhance the platform's performance, offering superior accuracy and efficiency in CL image analysis. A dataset of 600 experimental CL images was utilized, out of which 80% were used for model training, with 20% of the images reserved for testing. Comparative analysis was conducted using multiple deep learning models, including Random Forest, the Support Vector Machine (SVM), InceptionV3, VGG16, and ResNet-50, to identify the optimal architecture for accurate glucose detection. The CL sensor demonstrates a linear detection range of 10-1000 µM, with a low detection limit of 8.68 µM. Extensive evaluations confirmed its stability, repeatability, and reliability under real-world conditions. This deep learning-powered platform not only improves the accuracy of analyte detection, but also democratizes access to advanced diagnostics through cost-effective and portable technology. This work paves the way for next-generation biosensing, offering transformative potential in healthcare and other domains requiring rapid and reliable analyte detection.

Pop-Up Paper-Based Biosensor for a Dual-Mode Lung Cancer ctDNA Assay Based on Novel CoB Nanosheets with Dual-Enzyme Activities and a Portable Smartphone/Barometer for Readout

Timely monitoring of circulating tumor DNA (ctDNA) in serum is meaningful for personalized diagnosis and treatment for lung cancer. Cheap and efficient point-of-care testing (POCT) has emerged as a promising method, especially in a low-resource setting. Herein, (i) a 3D pop-up paper-based POCT device was designed and manufactured via a cheap method; it was used for saving time and efficiently building a biosensor; (ii) a novel cobalt boride nanosheet (CoB NS) nanozyme with abundant groups was used for POCT dual-mode signal transduction and then a portable smartphone/pressure meter to readout; (iii) a user-friendly smartphone app was fabricated for achieving more convenient POCT. Detailly, the dual-mode signal generated was based on the CoB NS with peroxidase activity to catalyze a chromogenic agent to develop color and with catalase activity to catalyze decomposition of H2O2 to O2. Density functional theory (DFT) and experimental results showed a good catalysis performance of the CoB NS via studying its five possible catalytic pathways, in which the metal Co is the catalytic active site center that acts as the electron donor and promotes electron transfer between the CoB NS and the adsorbed substrates. Benefiting from that, the proposed method showed good analytical ability in detecting ctDNA. Besides, its accuracy was valued by comparing it with the standard qPCR method to detect real samples from tumor cells and tumor-bearing mice, which showed a consistent result and potential practical applicability of the proposed method for POCT ctDNA. In general, this work not only provided a dual-mode POCT platform that could also be applied for other analytes but also first revealed the nanozyme properties of the CoB NS and inspired its new application from electrocatalysis, energy, etc. to biomedicine.

AI-optimized electrochemical aptasensors for stable, reproducible detection of neurodegenerative diseases, cancer, and coronavirus

AI-optimized electrochemical aptasensors are transforming diagnostic testing by offering high sensitivity, selectivity, and rapid response times. Leveraging data-driven AI techniques, these sensors provide a non-invasive, cost-effective alternative to traditional methods, with applications in detecting molecular biomarkers for neurodegenerative diseases, cancer, and coronavirus. The performance metrics outlined in the comparative table illustrate the significant advancements enabled by AI integration. Sensitivity increases from 60 to 75 % in ordinary aptasensors to 85-95 %, while specificity improves from 70-80 % to 90-98 %. This enhanced performance allows for ultra-low detection limits, such as 10 fM for carcinoembryonic antigen (CEA) and 20 fM for mucin-1 (MUC1) using Electrochemical Impedance Spectroscopy (EIS), and 1 pM for prostate-specific antigen (PSA) with Differential Pulse Voltammetry (DPV). Similarly, Square Wave Voltammetry (SWV) and potentiometric sensors have detected alpha-fetoprotein (AFP) at 5 fM and epithelial cell adhesion molecule (EpCAM) at 100 fM, respectively. AI integration also enhances reproducibility, reduces false positives and negatives (from 15-20 % to 5-10 %), and significantly decreases response times (from 10-15 s to 2-3 s). These advancements improve data processing speeds (from 10 to 20 min per sample to 2-5 min) and calibration accuracy (<2 % margin of error compared to 5-10 %), while expanding application scope to multi-target biomarker detection. This review highlights how these advancements position AI-optimized electrochemical aptasensors as powerful tools for personalized treatment, point-of-care testing, and continuous health monitoring. Despite a higher cost ($500-$1,500/unit), their enhanced portability and diagnostic performance promise to revolutionize healthcare, environmental monitoring, and food safety, ultimately improving public health outcomes.

Machine Learning-Driven Innovations in Microfluidics

Microfluidic devices have revolutionized biosensing by enabling precise manipulation of minute fluid volumes across diverse applications. This review investigates the incorporation of machine learning (ML) into the design, fabrication, and application of microfluidic biosensors, emphasizing how ML algorithms enhance performance by improving design accuracy, operational efficiency, and the management of complex diagnostic datasets. Integrating microfluidics with ML has fostered intelligent systems capable of automating experimental workflows, enabling real-time data analysis, and supporting informed decision-making. Recent advances in health diagnostics, environmental monitoring, and synthetic biology driven by ML are critically examined. This review highlights the transformative potential of ML-enhanced microfluidic systems, offering insights into the future trajectory of this rapidly evolving field.

Emerging Trends in Integrated Digital Microfluidic Platforms for Next-Generation Immunoassays

Technologies based on digital microfluidics (DMF) have made significant advancements in the automated manipulation of microscale liquids and complex multistep processes. Due to their numerous benefits, such as automation, speed, cost-effectiveness, and minimal sample volume requirements, these systems are particularly well suited for immunoassays. In this review, an overview is provided of diverse DMF manipulation platforms and their applications in immunological analysis. Initially, droplet-driven DMF platforms based on electrowetting on dielectric (EWOD), magnetic manipulation, surface acoustic wave (SAW), and other related technologies are briefly introduced. The preparation of DMF is then described, including material selection, fabrication techniques and droplet generation. Subsequently, a comprehensive account of advancements in the integration of DMF with various immunoassay techniques is offered, encompassing colorimetric, direct chemiluminescence, enzymatic chemiluminescence, electrosensory, and other immunoassays. Ultimately, the potential challenges and future perspectives in this burgeoning field are delved into.

AI-Reinforced Wearable Sensors and Intelligent Point-of-Care Tests

Artificial intelligence (AI) techniques offer great potential to advance point-of-care testing (POCT) and wearable sensors for personalized medicine applications. This review explores the recent advances and the transformative potential of the use of AI in improving wearables and POCT. The integration of AI significantly contributes to empowering these tools and enables continuous monitoring, real-time analysis, and rapid diagnostics, thus enhancing patient outcomes and healthcare efficiency. Wearable sensors powered by AI models offer tremendous opportunities for precise and non-invasive tracking of physiological conditions that are essential for early disease detection and personalized treatments. AI-empowered POCT facilitates rapid, accurate diagnostics, making these medical testing kits accessible and available even in resource-limited settings. This review discusses the key advances in AI applications for data processing, sensor fusion, and multivariate analytics, highlighting case examples that exhibit their impact in different medical scenarios. In addition, the challenges associated with data privacy, regulatory approvals, and technology integrations into the existing healthcare system have been overviewed. The outlook emphasizes the urgent need for continued innovation in AI-driven health technologies to overcome these challenges and to fully achieve the potential of these techniques to revolutionize personalized medicine.

Deep Learning-Assisted Smartphone-Based Electrochemiluminescence Visual Monitoring Biosensor: A Fully Integrated Portable Platform

A novel, portable deep learning-assisted smartphone-based electrochemiluminescence (ECL) cost-effective (~10$) sensing platform was developed and used for selective detection of lactate. Low-cost, fast prototyping screen printing and wax printing methods with paper-based substrate were used to fabricate miniaturized single-pair electrode ECL platforms. The lab-made 3D-printed portable black box served as a reaction chamber. This portable platform was integrated with a smartphone and a buck-boost converter, eliminating the need for expensive CCD cameras, photomultiplier tubes, and bulky power supplies. This advancement makes this platform ideal for point-of-care testing applications. Foremost, the integration of a deep learning approach served to enhance not just the accuracy of the ECL sensors, but also to expedite the diagnostic procedure. The deep learning models were trained (3600 ECL images) and tested (900 ECL images) using ECL images obtained from experimentation. Herein, for user convenience, an Android application with a graphical user interface was developed. This app performs several tasks, which include capturing real-time images, cropping them, and predicting the concentration of required bioanalytes through deep learning. The device's capability to work in a real environment was tested by performing lactate sensing. The fabricated ECL device shows a good liner range (from 50 µM to 2000 µM) with an acceptable limit of detection value of 5.14 µM. Finally, various rigorous analyses, including stability, reproducibility, and unknown sample analysis, were conducted to check device durability and stability. Therefore, the developed platform becomes versatile and applicable across various domains by harnessing deep learning as a cutting-edge technology and integrating it with a smartphone.