Artificial Intelligence for improving decision making in bacterial infection management: a narrative review

Background Development of clinical decision support systems (CDSS) has been ongoing for over 60 years, more recently leveraging technologies like artificial intelligence (AI) and machine learning (ML). Intelligent CDSS addressing different stages of the infection management process offer great advantages in interpreting complex data and guiding clinical decision-making. Objectives We outline the current applications of AI-driven CDSS across the continuum of bacterial infection management, from prevention and diagnosis to antibiotic prescribing and treatment individualisation. We discuss the main limitations hindering their translation into clinical practice, as well as opportunities to improve their development to better meet clinical needs. Methods References for this review were identified through searches of PubMed, Google Scholar, biorXiv and arXiV up to March 2025 by use of a combination of ML, decision-making and bacterial infection keywords. Key Findings AI-CDSS studies increasingly leverage multimodal EHR data, with most adopting lower 57 complexity models that perform well on structured data, particularly when supported by effective feature engineering. Despite efforts to develop accurate AI-driven systems, some of which achieve clinician-level accuracy in solving diagnostic and prescribing tasks, AI-CDSS have largely failed to integrate into clinical settings. Their adoption faces challenges related to the narrow scope of the defined medical task, failure to consider stakeholder workflow, and lack of proper evaluation frameworks. Conclusion There is a need to shift CDSS development towards a more adaptive and holistic approach that recognises the continuous nature of the decision-making process in infection management. Comprehensive AI-powered platforms that can model infection dynamics could improve antibiotic stewardship and help tackle the global health emergency of antimicrobial resistance.