Artificial intelligence-based methods and applications in clinical and diagnostic microbiology: Current challenges and future perspectives.

Abstract

Microbiology laboratories play a critical role in the diagnosis and management of infectious diseases. However, recent advancements aimed at reducing human workload and minimizing time loss are gaining popularity. Artificial intelligence (AI) technologies, particularly machine learning (ML) and deep learning (DL), have been reported to contribute significantly to microbial laboratory diagnostics. Through this approach, molecular methods, genetic sequencing, microbiological meta-analyses, and related fields benefit from faster and more accurate analytic capabilities. In addition to diagnostic applications, AI is increasingly used in genomics, metagenomics, antimicrobial resistance (AMR) prediction, and drug and vaccine discovery, enabling more comprehensive and data-driven microbiological analysis. This review comprehensively evaluates current AI applications in microbiology, highlighting their advantages, limitations, and implementation challenges. It further examines the suitability of different AI methodologies for specific laboratory tasks and compares AI-driven approaches with conventional expert-based practices. Finally, the study emphasizes the complementary roles of AI systems and human expertise, underscoring their synergistic potential to improve diagnostic accuracy, efficiency, and clinical decision-making.