ReviewACS sensors2024
Role of Machine Learning Assisted Biosensors in Point-of-Care-Testing For Clinical Decisions.
Review in ACS sensors, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
60 citing papers in PubMed, 1 synthesis or guideline pooled it.
- From Conventional Detection to Point-of-care Tests (POCT) Method for Pediatric Respiratory Infections Diagnosis: A Systematic Review.Archives of Iranian medicine · 2025Pooled it
- Wearable Electronics for Precision Diagnosis Through Advanced Manufacturing and Integration.Nano-micro letters · 2026Review
- Portable Sensing Systems in Biological and Chemical Analyses: A Review of Sensor Technologies, Miniaturized Platforms, Data Processing, and Field Applications.Micromachines · 2026Review
- Recent Progress in Artificial Intelligence in Biosensor Development: From Bioprobe Design to Fabrication and Signal Analysis.Biosensors · 2026Review
- Rationally engineered icosahedral-spindle aluminum-based metal-organic frameworks with dual-state luminescence for on-site detection of ferric ions in food and human serum.Mikrochimica acta · 2026Article
- AI-driven multimodal retinal imaging for early detection and risk stratification of vascular and neurodegenerative diseases.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Emerging Trends in Artificial Intelligence-Integrated Biochip Technologies for Biomedical Applications.Micromachines · 2026Review
- Machine learning-assisted paper-based chemiluminescence biosensor for choline quantification in infant milk: toward portable nutritional quality monitoring.Scientific reports · 2026Article
- AI-Assisted Molecular Biosensors: Design Strategies for Wearable and Real-Time Monitoring.International journal of molecular sciences · 2026Review
- Biosensor-Integrated Microneedle Devices for Diagnosis and Treatment of Chronic and Infectious Diseases: Current Status, Trends and Challenges.Biosensors · 2026Review
- Electrochemical Biosensing Platforms for Rapid and Early Diagnosis of Crop Fungal and Viral Diseases.Sensors (Basel, Switzerland) · 2026Review
- Review
- Biosensing technologies for foodborne pathogen detection and healthcare: principles, emerging materials, and intelligent platforms.Mikrochimica acta · 2026Review
- Emerging liquid biopsy tools to analyse cancer biomarkers: electrochemical sensors and extracellular vesicle analysis.Biochemical Society transactions · 2026Review
- Overview in Machine-Learning-Assisted Sensing Techniques for Monitoring COVID-19.Micromachines · 2026Review
- AI-driven routing and layered architectures for intelligent ICT in nanosensor networked systems.iScience · 2026Review
- Sensitivity Enhancement of Multiplex Lateral Flow Immunoassays by NIR-II Fluorescence and Thermal Contrast.Analytical chemistry · 2026Article
- Artificial neural network modeling and optimization of an electrochemical biosensor for plasma miR-155-based breast cancer detection.Scientific reports · 2026Article
- Recent Advances in Microfluidic Chip Technology for Laboratory Medicine: Innovations and Artificial Intelligence Integration.Biosensors · 2026Review
- Glycomic Insights in Gynecological Disease: From Molecular Mechanisms to Precision Diagnostics and Therapeutics.International journal of molecular sciences · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Point-of-Care-Testing (PoCT) has emerged as an essential component of modern healthcare, providing rapid, low-cost, and simple diagnostic options. The integration of Machine Learning (ML) into biosensors has ushered in a new era of innovation in the field of PoCT. This article investigates the numerous uses and transformational possibilities of ML in improving biosensors for PoCT. ML algorithms, which are capable of processing and interpreting complicated biological data, have transformed the accuracy, sensitivity, and speed of diagnostic procedures in a variety of healthcare contexts. This review explores the multifaceted applications of ML models, including classification and regression, displaying how they contribute to improving the diagnostic capabilities of biosensors. The roles of ML-assisted electrochemical sensors, lab-on-a-chip sensors, electrochemiluminescence/chemiluminescence sensors, colorimetric sensors, and wearable sensors in diagnosis are explained in detail. Given the increasingly important role of ML in biosensors for PoCT, this study serves as a valuable reference for researchers, clinicians, and policymakers interested in understanding the emerging landscape of ML in point-of-care diagnostics.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.