Harnessing AI and Bioinformatics to Combat Methicillin-Resistant Staphylococcus Aureus: Innovations in Genomic Surveillance, Diagnosis, and Therapeutic Development

The global rise of antimicrobial resistance (AMR), particularly methicillin-resistant Staphylococcus aureus (MRSA), poses a major health challenge, demanding innovative strategies for surveillance, diagnosis, and therapy. Advances in artificial intelligence (AI), machine learning (ML), and bioinformatics are transforming infectious disease management by enabling high-throughput analysis of genomic, clinical, and epidemiological data. This review explores the application of AI and bioinformatics in combating MRSA, focusing on early detection, resistance mechanism analysis, and development of targeted therapies. Key platforms, including whole-genome sequencing (WGS) workflows, pan-genome analysis, and molecular typing tools, are discussed alongside AI-driven innovations in risk assessment, antimicrobial peptide design, drug repurposing, and resistance modelling. Case studies such as Next Gen Diagnostics, DeepARG, and PyTorch_EHR highlight improved diagnostic accuracy, reduced turnaround time, and support for empirical treatment. The review also addresses challenges to clinical implementation and emphasizes the future potential of AI-integrated approaches for precision medicine and effective MRSA control. Graphical abstract showing the applications of AI/ML in the management of MRSA.