Abstract
Traditional bacterial identification methods—such as culture-based assays, biochemical tests, and manual microscopy—are often slow, labor-intensive, and lack precision, posing significant challenges in clinical, food safety, and environmental applications. Artificial intelligence (AI) offers transformative solutions by dramatically improving speed, accuracy, and automation. This systematic review comprehensively evaluates AI-driven techniques for bacterial identification, focusing on three core technological domains: advanced imaging (including microscopy and hyperspectral systems), spectroscopic methods (such as Raman and FTIR), and sensor array technologies integrated with machine learning (ML) and deep learning (DL). We analyzed 70 peer-reviewed studies published between 2018 and 2025, sourced from PubMed, IEEE Xplore, and Scopus. Findings reveal that AI models consistently achieve high classification accuracies, ranging from 85.8% to 99%, enabling rapid detection of pathogens, profiling of antibiotic resistance, and point-of-care diagnostics. Deep learning, particularly convolutional neural networks (CNNs), excels in image analysis, while spectroscopy provides non-destructive molecular fingerprinting. Despite these advances, key challenges remain, including reliance on small or non-standardized datasets, high computational demands, and the prohibitive cost of specialized equipment. To realize AI’s full potential, future efforts must prioritize the development of lightweight, efficient models, the creation of large, diverse, and open-source datasets, and the design of low-cost, portable diagnostic platforms. This review not only highlights AI’s current capabilities but also identifies critical barriers and charts a clear path for future research to enable the scalable, real-world deployment of AI across global healthcare and industrial settings.
Keywords: Advanced Imaging, Bacterial Identification, Deep Learning, Hyperspectral Imaging, Sensor Array, Spectroscopy