Artificial intelligence and antimicrobial stewardship: From clinical decision support to public health action-On behalf of APUA

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Authors: Abdul Ghafur, Miki Nagao, Souha Sami Kanj, Salam Abbara, Pierre Tattevin, Mariette Awad, Maya Dagher, Linus Ndegwa, Gathai Mundia, Jeroen Schouten, Vu Thi Lan Huong, Gabriel Levy Hara
+5 more Maneesh Paul Satyaseela, Mamoon A. Aldeyab, Raksha K. Bhat, Nitin Bansal, Heiman F. L. Wertheim
Cited by: 1
References: 25

Abstract

BACKGROUND: Antimicrobial resistance continues to threaten the effectiveness of modern medicine and poses major public health challenges worldwide. Antimicrobial stewardship (AMS) programmes are essential to mitigate inappropriate antimicrobial use but are often resource-intensive and difficult to sustain. Advances in artificial intelligence (AI) have generated interest in its potential to support stewardship activities across clinical, operational, and public health domains. OBJECTIVES: To critically review the current and emerging roles of AI in AMS and to examine its opportunities, limitations, and implications for clinical practice, public health, and health systems globally. METHODS: We conducted a narrative review of the literature on AI applications relevant to AMS, including diagnostic support, antimicrobial prescribing, behavioural and implementation science, infection prevention, surveillance, One Health approaches, drug discovery, and governance. Particular attention was paid to ethical considerations, human oversight, and applicability in low- and middle-income country settings. RESULTS: AI has demonstrated potential to enhance diagnostic precision, support clinical decision-making, optimise antimicrobial use, strengthen surveillance systems, and inform targeted stewardship interventions. However, challenges related to data quality, bias, interpretability, workflow integration, ethical governance, and equity remain significant. AI systems developed in high-income settings may not be directly transferable to low- and middle-income countries without careful adaptation and local validation. CONCLUSIONS: AI should be viewed as a decision-support tool that augments, rather than replaces, AMS expertise. When implemented responsibly within robust governance frameworks and aligned with AMS principles, AI can contribute meaningfully to improved antimicrobial use and antimicrobial resistance control. Sustained human leadership, equity-focused deployment, and health system strengthening remain essential.

Fields of Study & Subjects

Antibiotic Use and Resistance Bacterial Identification and Susceptibility Testing Artificial Intelligence in Healthcare and Education

Publication details

Journal / source
Journal of Global Antimicrobial Resistance
Publisher
Elsevier BV
Volume
50
Pages
451–459
Publication date
6 August 2026
Language
EN