1)The global rise of antimicrobial resistance (AMR) poses a profound threat to human, animal and environmental health. Although antimicrobials have revolutionized modern medicine, their overuse and misuse have accelerated AMR, necessitating urgent, multisectoral action. Antimicrobial stewardship (AMS), a set of coordinated strategies that promote responsible antimicrobial use, has emerged as a key intervention in managing AMR. In this Review, we explore AMS within a One Health framework, emphasizing interconnectedness across sectors. We examine clinical, economic, sociocultural and environmental drivers of antimicrobial use, highlighting disparities between high-income and low-income settings and identifying context-specific challenges to implementation. We also discuss the importance of governance, financing, digital innovation, surveillance and behavioural science in shaping sustainable AMS programmes, and we consider core components, such as policy integration, surveillance of appropriateness, and context-aware interventions. This Review ultimately advocates for equity-focused strategies that better account for structural barriers, support marginalized populations, and ensure global access to high-quality antimicrobials. By aligning political will, funding and scientific innovation, AMS programmes can be scaled effectively to preserve antimicrobial efficacy, mitigate AMR, improve health outcomes, and promote global health security. The paper concludes with key recommendations for embedding AMS across sectors as a sustainable response to AMR. 2)Antimicrobial resistance (AMR) disproportionately affects people who are immunocompromised due to their frequent encounters with the health-care system and repeated, prolonged exposure to antibiotics. AMR threatens to undermine continued advances in cancer care, haematopoietic cell transplantation, and solid organ transplantation by severely restricting therapeutic options. The convergence of several factors in the diagnostic evaluation of infection among individuals with immunocompromising conditions contributes to excess and inappropriate antibiotic use. Diagnostic and antimicrobial stewardship are key complementary strategies to address these challenges with shared goals of improving patient outcomes, reducing harm, and mitigating the risk of AMR. In this Series paper, we discuss opportunities to enhance use of existing diagnostic tools (eg, culture-based diagnostics, molecular diagnostics, and other tools such as antibiotic allergy delabelling), emerging diagnostic tools (eg, metagenomic sequencing and host response profiling), and digital innovation, to optimise antibiotic use, and the potential for precision medicine approaches to combat AMR in people who are immunocompromised. 3)Our results revealed that ML tools offer promising enhancements to traditional AMS strategies. However, high heterogeneity, inconsistent results between fixed and random effect models, and limited use of external validation in retrieved studies raise concerns about the generalizability of the findings. Furthermore, the lack of representation from outpatient and pediatric settings highlights a critical equity gap in the application of these technologies. 4)## link.springer.com Artificial intelligence in antimicrobial stewardship: a systematic review and meta-analysis of predictive performance and diagnostic accuracy Flavia Pennisi, Antonio Pinto, Giovanni Emanuele Ricciardi, Carlo Signorelli, Vincenza Gianfredi European Journal of Clinical Microbiology & Infectious Diseases 44 (3), 463-513, 2025 The increasing threat of antimicrobial resistance has prompted a need for more effective antimicrobial stewardship programs (AMS). Artificial intelligence (AI) and machine learning (ML) tools have emerged as potential solutions to enhance decision-making and improve patient outcomes in AMS. This systematic review and meta-analysis aims to evaluate the impact of AI in AMS and to assess its predictive performance and diagnostic accuracy. We conducted a comprehensive literature search across PubMed/MEDLINE, Scopus, EMBASE, and Web of Science to identify studies published up to July 2024. Studies included were observational, cohort, or retrospective, focusing on the application of AI/ML in AMS. The outcomes assessed were the area under the curve (AUC), accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). We calculated the mean pooled effect size (ES) and its 95% confidence interval (CI) using a random-effects model. The risk of bias was assessed using the QUADAS-AI tool, and the protocol was registered in PROSPERO. Out of 3,458 retrieved articles, 80 studies met the inclusion criteria. Our meta-analysis demonstrated that ML models exhibited strong predictive performance and diagnostic accuracy, with the following results: AUC [ES: 72.28 (70.42–74.14)], accuracy [ES: 74.97 (73.35–76.58)], sensitivity [ES: 76.89; (71.90–81.89)], specificity [ES: 73.77; (67.87–79.67)], NPV [ES:79.92 (76.54–83.31)], and PPV [ES: 69.41 (60.19–78.63)] across various AMS settings. AI and ML tools offer promising enhancements due to their strong predictive performance. The integration of AI into AMS could lead to more precise antimicrobial prescribing, reduced antimicrobial resistance, and better resource utilization. View at link.springer.com Cited by 56 Related articles All 9 versions sciencedirect.com Evaluating the impact of artificial intelligence in antimicrobial stewardship: a comparative meta-analysis with traditional risk scoring systems Antonio Pinto, Flavia Pennisi, Giovanni Emanuele Ricciardi, Carlo Signorelli, Vincenza Gianfredi Infectious Diseases Now 55 (5), 105090, 2025 Objectives The growing challenge of antimicrobial resistance (AMR) has underscored the urgent need for robust antimicrobial stewardship programs (AMS). Artificial intelligence (AI) and machine learning (ML) have emerged as promising tools to support enhanced decision-making in AMS. This systematic review and meta-analysis aims to evaluate the impact of AI in AMS and compare its effectiveness with traditional risk systems. Methods PubMed/MEDLINE, Scopus, EMBASE, and Web of Science were searched to identify studies published up to July 2024. Any studies that evaluated the use of AI/ML in AMS compared with conventional decision-making approaches were eligible. Outcomes of interested were predictive performance metrics and diagnostic accuracy. The meta-estimate was performed pooling standardized mean difference, and effect size (ES) measured as Cohen’s d with a 95% confidence interval (CI). The risk of bias was assessed using the QUADAS-AI tool. Results Out of 3,458 studies, 27 were included, demonstrating that ML models outperform traditional methods in terms of sensitivity [1.93 (0.48–3.39) p = 0.009], and negative predictive value [1.66 (0.86–2.46), p < 0.001] but not in terms of area under the curve, accuracy, specificity, positive predictive value, when random effect models were applied. Conclusions Our results revealed that ML tools offer promising enhancements to traditional AMS strategies. However, high heterogeneity, inconsistent results between fixed and random effect models, and limited use of external validation in retrieved studies raise concerns about the generalizability of the findings. Furthermore, the lack of representation from outpatient and pediatric settings highlights a critical equity gap in the application of these technologies. View at sciencedirect.com [HTML] sciencedirect.com Cited by 31 Related articles All 8 versions sciencedirect.com A systematic review of antimicrobial stewardship interventions implemented in intensive care units OK Ntim, B Opoku-Asare, ES Donkor Journal of Hospital Infection 162, 272-283, 2025 Antimicrobial stewardship (AS) is essential to ensure appropriate antimicrobial usage and subsequently reduce the emergence of microbial resistance. The intensive care unit is a crucial place for monitoring antimicrobial usage due the frequent exposure to infections. This review provides an update on the current status of AS intervention utilized in intensive care settings. A comprehensive search was performed in Cochrane, Web of Science, and PubMed using keywords grouped into Antimicrobial, Stewardship, and Intensive care unit. The search was restricted to original articles published from April 2015 to November 2024. Of 1234 records retrieved from the databases, 55 studies were included in this systematic review. Most of the studies were conducted in the USA (N = 9), followed by China (N = 8), India (N = 5), and Italy (N = 4). We identified seven key AS strategies: multi-intervention AS programmes (22 studies, 40%), prospective feedback and audit (11 studies, 20%), procalcitonin (PCT) protocols for guiding antimicrobial use (12 studies, 21.8%), protocols for antimicrobial de-escalation (four studies, 7.3%), antimicrobial restrictions or preapprovals (four studies, 7.3%), diagnostic stewardship (one study, 1.8%), and guidelines for antimicrobial prescription (one study, 1.8%). A reduction in targeted or overall antimicrobial usage was reported in most studies (34/42). Specifically, all studies implementing multi-invention AS programmes reported a successful reduction in antimicrobial utilization. Some AS interventions significantly enhanced the appropriateness of antimicrobial prescriptions. In addition, patient health outcomes were not compromised by antimicrobial reduction. Nonetheless, future studies at a larger scale over a longer time are recommended to accurately assess the impact of AS programme on patient health outcomes. Promt: Analyze these research article data and find if there is anything that is untouched by the research scientist and study the pattern for me.