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

IF 3.7 3区 医学 Q2 INFECTIOUS DISEASES
Abdul Ghafur, Miki Nagao, Souha S. Kanj, Salam Abbara, Pierre Tattevin, Mariette Awad, Maya Dagher, Linus K. Ndegwa, Gathai Mundia, Jeroen Schouten, Huong T.L. Vu, Gabriel Levy Hara, Maneesh Paul Satyaseela, Mamoon A. Aldeyab, Raksha K. Bhat, Nitin Bansal, Heiman F.L. Wertheim
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引用次数: 0

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.

Abstract Image

人工智能和抗菌药物管理:从临床决策支持到公共卫生行动。
背景:抗菌素耐药性(AMR)继续威胁着现代医学的有效性,并在世界范围内构成重大的公共卫生挑战。抗菌素管理(AMS)规划对于减少不适当使用抗菌素至关重要,但往往是资源密集型的,难以维持。人工智能(AI)的进步使人们对其支持临床、业务和公共卫生领域管理活动的潜力产生了兴趣。目的:批判性地回顾人工智能在AMS中当前和新兴的作用,并研究其对临床实践、公共卫生和全球卫生系统的机会、局限性和影响。方法:我们对与AMS相关的人工智能应用的文献进行了叙述性回顾,包括诊断支持、抗菌药物处方、行为和实施科学、感染预防、监测、One Health方法、药物发现和治理。特别注意了伦理考虑、人为监督以及在中低收入国家(LMIC)环境中的适用性。结果:人工智能已显示出在提高诊断精度、支持临床决策、优化抗菌药物使用、加强监测系统和为有针对性的管理干预提供信息方面的潜力。然而,与数据质量、偏见、可解释性、工作流集成、道德治理和公平性相关的挑战仍然很大。在高收入环境中开发的人工智能系统可能无法在没有仔细适应和当地验证的情况下直接转移到中低收入国家。结论:人工智能应被视为一种决策支持工具,可以增强而不是取代AMS的专业知识。如果在强有力的治理框架内以负责任的方式实施,并与AMS原则保持一致,人工智能可以为改善抗菌药物使用和抗生素耐药性控制做出有意义的贡献。持续的人力领导、以公平为重点的部署和加强卫生系统仍然至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of global antimicrobial resistance
Journal of global antimicrobial resistance INFECTIOUS DISEASES-PHARMACOLOGY & PHARMACY
CiteScore
8.70
自引率
2.20%
发文量
285
审稿时长
34 weeks
期刊介绍: The Journal of Global Antimicrobial Resistance (JGAR) is a quarterly online journal run by an international Editorial Board that focuses on the global spread of antibiotic-resistant microbes. JGAR is a dedicated journal for all professionals working in research, health care, the environment and animal infection control, aiming to track the resistance threat worldwide and provides a single voice devoted to antimicrobial resistance (AMR). Featuring peer-reviewed and up to date research articles, reviews, short notes and hot topics JGAR covers the key topics related to antibacterial, antiviral, antifungal and antiparasitic resistance.
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