The Role of Artificial Intelligence in Addressing Antimicrobial Resistance: Challenges, Opportunities, and Strategic Interventions

Abstract

AIMR is an emerging global health problem which affects the efficacy of critical medicines and treatment failure in clinical and public-health settings. This review provides insights into the current role of AI in tackling antimicrobial resistance across the four A's of surveillance, diagnostics, antimicrobial stewardship and drug discovery, as well as in the integration of One Health. The paper reviews the evidence, using a narrative review format and a scoping review approach with principles, synthesizing evidence from the global burden studies, clinical microbiology, machine learning research, stewardship literature and antimicrobial innovation frameworks. The review highlights the potential of using AI to enhance the use of AMR response, such as identifying resistance trends at an earlier stage, forecasting risk of resistance at the patient level, speeding up interpretation of diagnostics, improve antimicrobial prescribing, and broaden the exploration of new antimicrobials. But adoption of AI is still hindered by siloed health data systems, scarce external validation, algorithmic bias, poor interoperability of AI systems, regulatory ambiguity and poor infrastructure within health systems. The paper concludes that AI should be used as a clinically informed decision-making support infrastructure, not as a substitute for the knowledge and experience of a clinical team, laboratory capacity, or public-health stewardship.