From Guidelines to Real-Time Guardrails: The Emerging Role of AI in AMR Surveillance and IPC Decision-Making

Antimicrobial resistance (AMR) and healthcare-associated infections (HAIs) represent escalating, interlinked global health crises demanding urgent, innovative solutions. While comprehensive clinical practice guidelines for antimicrobial stewardship (AMS) and infection prevention and control (IPC) exist, their translation into consistent, effective clinical practice remains challenging due to information overload, data fragmentation, cognitive burden, and the static nature of traditional guidance. The maturation of advanced computational systems, particularly those leveraging machine learning (ML), natural language processing (NLP), large language models (LLMs), and automation (collectively termed AI), presents a transformative opportunity. This paper explores the paradigm shift facilitated by these systems, moving beyond passive support towards active agency in adaptive, real-time decision-making for AMR surveillance, AMS, and IPC. Through a critical analysis of current literature, including recent comprehensive global burden assessments, examination of emerging capabilities, and exploration of practical use cases, we propose a conceptual framework where computational systems underpin dynamic, context-aware "guardrails." These aim to support timely, evidence-based decisions, foster federated data analysis for enhanced surveillance informed by real-world burden data, promote equitable access to insights, and strengthen global resilience against specific high-burden pathogens and resistance patterns. However, significant challenges remain, including the need for robust, multi-stage validation processes akin to therapeutic development, managing algorithmic bias, ensuring data privacy, navigating complex ethical and medico-legal landscapes, optimizing human-AI interaction to manage cognitive load, and addressing disparities in regulatory frameworks and resources across nations, which risk creating inequities in AI adoption and hindering global AMR control. We advocate for rigorous validation defining safety and efficacy, enhanced clinician training integrated with human factors engineering, and strengthened international collaboration to ensure the safe, fair, and effective deployment of these powerful systems globally, aligned with demonstrated needs and intervention potentials.