AI-microbial hybrid biosensors: the next generation of intelligent detection systems

The convergence of artificial intelligence (AI) and microbial biosensor technology is transforming pathogen detection, environmental surveillance, antimicrobial resistance (AMR) profiling, and precision diagnostics. Microbial biosensors exploit the specificity of living microorganisms, but signal variability, scalability limits, and interpretive challenges have constrained clinical adoption. Integration of machine learning (ML) and deep neural networks (DNNs) now enables adaptive, high-performance sensing systems. Applied to multi-sensor datasets-such as electrochemical impedance, Raman spectroscopy, and hyperspectral microscopy-convolutional neural networks (CNNs) and ensemble models achieve bacterial classification accuracies of 95-99%, while markedly reducing diagnostic turnaround times and enabling continuous surveillance. Despite rapid progress, the field remains fragmented, lacking a unified synthesis of system architectures, computational strategies, translational barriers, and regulatory considerations. This narrative review provides an integrative analysis of AI-microbial hybrid biosensors, covering biorecognition principles, AI integration approaches, system designs, clinical and environmental applications, performance metrics, and key challenges. It also highlights emerging directions, including synthetic biology, CRISPR-enabled sensing, and edge computing. By consolidating these dimensions, this review positions AI-microbial hybrid biosensors as a next-generation platform for real-time pathogen detection and adaptive biosurveillance. Literature was identified through systematic searches of Google Scholar, PubMed, Web of Science, Scopus, and IEEE Xplore (2000-2026), supplemented by manual reference screening.