ReviewBiosensors2025
AI-Enhanced Electrochemical Sensing Systems: A Paradigm Shift for Intelligent Food Safety Monitoring.
Review in Biosensors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Sustainable smart sensing and AI-driven platforms for real-time detection and monitoring of mycotoxins across the food supply chain.Mycotoxin research · 2026Pooled it
- Analytical applications of molecularly imprinted polymers: a personal view.Analytical and bioanalytical chemistry · 2026Review
- From laboratory to field: a critical review of multi-modal sensing frameworks for emerging contaminant monitoring.Mikrochimica acta · 2026Review
- Leakage-Free Benchmarking of Electronic Noses for Beef Freshness: A Signal-Richness Criterion for Model Selection.Foods (Basel, Switzerland) · 2026Article
- Artificial Intelligence in Foodborne Pathogen Detection from Sensing to Food Safety Systems: A Systematic Review.Foods (Basel, Switzerland) · 2026Review
- Recent Progress in Artificial Intelligence in Biosensor Development: From Bioprobe Design to Fabrication and Signal Analysis.Biosensors · 2026Review
- Phytochemical Engineering of Alternative Plant Proteins for Enhanced Nutrition and Digestibility.Plant foods for human nutrition (Dordrecht, Netherlands) · 2026Review
- Advanced biosensing strategies for high-risk foodborne pathogens: a comprehensive review ofFood chemistry. Molecular sciences · 2026Review
- Advances in fluorescence-based point-of-care diagnostics: probes, nanostructures and integrated systems.Journal of materials chemistry. C · 2026Review
- Artificial intelligence in microbiology: implications for metagenomics, diagnostics, and AMR surveillance.Biomedical engineering online · 2026Review
- Review
- Nanozymes Integrated Biochips Toward Smart Detection System.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Next-generation viral detection through AI-enhanced nanotechnology: advances, challenges, and future directions.Frontiers in molecular biosciences · 2026Article
- AI-Assisted Impedance Biosensing of Yeast Cell Concentration.Biosensors · 2025Article
- Transforming cytokine diagnostics: AI, multiplexing, and point-of-care biosensing technologies.Mikrochimica acta · 2025Review
- Review
- Integration of Artificial Intelligence in Biosensors for Enhanced Detection of Foodborne Pathogens.Biosensors · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
Abstract
Artificial intelligence (AI) is transforming electrochemical biosensing systems, offering novel solutions for foodborne pathogen detection. This review examines the integration of AI technologies, particularly machine learning and deep learning algorithms, in enhancing sensor design, material optimization, and signal processing for detecting key pathogens such as
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.