{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T06:59:56Z","timestamp":1793170796587,"version":"4.5.0"},"reference-count":106,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T00:00:00Z","timestamp":1738195200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Antibiotics"],"abstract":"<jats:p>Antimicrobial resistance (AMR) poses a critical global health threat, necessitating innovative approaches in antimicrobial stewardship (AMS). Artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools in this domain, enabling data-driven interventions to optimize antibiotic use and combat resistance. This comprehensive review explores the multifaceted role of AI and ML models in enhancing antimicrobial stewardship efforts across healthcare systems. AI-powered predictive analytics can identify patterns of resistance, forecast outbreaks, and guide personalized antibiotic therapies by leveraging large-scale clinical and epidemiological data. ML algorithms facilitate rapid pathogen identification, resistance profiling, and real-time monitoring, enabling precise decision making. These technologies also support the development of advanced diagnostic tools, reducing the reliance on broad-spectrum antibiotics and fostering timely, targeted treatments. In public health, AI-driven surveillance systems improve the detection of AMR trends and enhance global monitoring capabilities. By integrating diverse data sources\u2014such as electronic health records, laboratory results, and environmental data\u2014ML models provide actionable insights to policymakers, healthcare providers, and public health officials. Additionally, AI applications in antimicrobial stewardship programs (ASPs) promote adherence to prescribing guidelines, evaluate intervention outcomes, and optimize resource allocation. Despite these advancements, challenges such as data quality, algorithm transparency, and ethical considerations must be addressed to maximize the potential of AI and ML in this field. Future research should focus on developing interpretable models and fostering interdisciplinary collaborations to ensure the equitable and sustainable integration of AI into antimicrobial stewardship initiatives.<\/jats:p>","DOI":"10.3390\/antibiotics14020134","type":"journal-article","created":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T04:01:39Z","timestamp":1738209699000},"page":"134","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":107,"title":["The Role of Artificial Intelligence and Machine Learning Models in Antimicrobial Stewardship in Public Health: A Narrative Review"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-9185-9747","authenticated-orcid":false,"given":"Flavia","family":"Pennisi","sequence":"first","affiliation":[{"name":"Faculty of Medicine, University Vita-Salute San Raffaele, 20132 Milan, Italy"},{"name":"PhD National Program in One Health Approaches to Infectious Diseases and Life Science Research, Department of Public Health, Experimental and Forensic Medicine, University of Pavia, 27100 Pavia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4315-0481","authenticated-orcid":false,"given":"Antonio","family":"Pinto","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, University Vita-Salute San Raffaele, 20132 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6903-655X","authenticated-orcid":false,"given":"Giovanni Emanuele","family":"Ricciardi","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, University Vita-Salute San Raffaele, 20132 Milan, Italy"},{"name":"PhD National Program in One Health Approaches to Infectious Diseases and Life Science Research, Department of Public Health, Experimental and Forensic Medicine, University of Pavia, 27100 Pavia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9710-4962","authenticated-orcid":false,"given":"Carlo","family":"Signorelli","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, University Vita-Salute San Raffaele, 20132 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3848-981X","authenticated-orcid":false,"given":"Vincenza","family":"Gianfredi","sequence":"additional","affiliation":[{"name":"Department of Biomedical Sciences for Health, University of Milan, Via Pascal 36, 20133 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1016\/S0140-6736(24)01867-1","article-title":"Global burden of bacterial antimicrobial resistance 1990\u20132021: A systematic analysis with forecasts to 2050","volume":"404","author":"Naghavi","year":"2024","journal-title":"Lancet"},{"key":"ref_2","unstructured":"World Health Organization (WHO) (2024, December 04). 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