Antimicrobial resistance (AMR) is a serious global health threat caused by the overuse and misuse of antibiotic resulting in treatment failure. The current conventional techniques have various constraints requiring specialized expertise, longer turnaround times requiring rapid point of care and transformative solutions. This narrative review explores the applications of artificial intelligence (AI) in AMR diagnostics. A structured search of PubMed, Web of Science, and Google Scholar was conducted using the MeSH terms. Relevant studies were screened and synthesized in four themes; phenotypic and genotypic identification, antimicrobial susceptibility testing (AST), AMR surveillance, and antibiotic development. The reporting was guided by the Scale for the Assessment of Narrative Review Articles (SANRA). Across AMR diagnostic, machine learning and deep learning improved the accuracy, reproducibility, and scalability of bacterial identification by learning complex patterns of AMR. The AI models utilized wide variety of data including genomic profiles, radiological imaging, microscopy, agar plate photography, and biochemical signatures such as MALDI TOF mass spectrometry. In Antibiotic Susceptibility Tests (AST), AI helped in standardizing the interpretation of disc diffusion and MIC assays. In AMR surveillance, AI models supported screening and genomic detection of resistance determinants, which enabled the identification of resistance trends and policy evaluations of One Health integration. In antibiotic development, AI contributed to therapeutic discoveries through screening large chemical libraries and designing antimicrobial peptides or adjuvants with reduced experimental burden. The reviewed evidence indicates that AI substantially enhances decision making in scope of AMR challenges. Effectively continued impact will depend on data quality, model development and integration into public health and laboratory infrastructures.
Hassan et al. (Sun,) studied this question.