AI-Driven Strategies to Forecast and Combat Antibiotic Resistance

Antimicrobial resistance (AMR) is a pressing global health emergency fuelled by the overuse and misuse of antibiotics and threatens human and animal health. Conventional diagnostic and therapeutic approaches tend to be slow, resource-consuming, and of limited predictive ability. Artificial intelligence (AI) and machine learning (ML) advancements provide disruptive potential to predict, prevent, and regulate AMR. Artificial intelligence -driven models can handle and analyse complex, advanced clinical, genomic, and epidemiologic information to predict patterns of resistance, guide antibiotic selection, and maximize stewardship programs. Applications range from rapid diagnostic testing, decision support systems, and drug discovery platforms to novel approaches like AI-assisted antimicrobial peptide design and nanoparticle therapeutics. . Promising as these are, there are barriers to their uptake in the form of data quality, model bias, explain ability, infrastructure requirements, and regulatory adoption. This review integrates state-of-the-art, technology innovation, and prospective AI-based AMR management and emphasizes the need for multidisciplinary team effort in facilitating innovation and harvesting AI potential into clinical and public health applications.