Integrating machine learning and artificial intelligence in the management of Acinetobacter infections: a narrative review.

Acinetobacter baumannii, particularly in its multidrug-resistant (MDR) and carbapenem-resistant (CRAB) forms, has become a major global health concern due to its ability to survive in hospital environments, acquire resistance rapidly, and cause severe infections with high mortality. Conventional diagnostic and therapeutic strategies remain slow, imprecise, and difficult to implement, especially in resource-limited settings, highlighting the need for innovative approaches. Advances in artificial intelligence (AI) and machine learning (ML) offer powerful tools to strengthen every stage of Acinetobacter infection management. This narrative review synthesizes current evidence on how AI enhances early detection, improves species-level identification, predicts antimicrobial resistance from genomic data, accelerates drug discovery, and supports real-time hospital surveillance and outbreak control. AI-driven methods enable faster triage, more accurate differentiation between colonization and true infection, robust prediction of resistance phenotypes, and efficient identification of synergistic antibiotic combinations. Moreover, AI-supported drug discovery pipelines have recently yielded novel agents such as abaucin, demonstrating the potential of computational approaches to explore new chemical spaces. Despite these promising advances, challenges persist regarding data quality, generalizability across settings, interpretability, and ethical considerations including privacy and algorithmic bias. Successful integration of AI into clinical practice will require rigorous model validation, equitable data governance, and strong collaboration between clinicians, microbiologists, and data scientists. Overall, AI represents a transformative opportunity to reduce the clinical and economic burden of Acinetobacter infections and to strengthen global antimicrobial resistance surveillance.