Role of Artificial Intelligence and Real-Time Clinical Decision Support System in Enhancing Antimicrobial Stewardship for Pneumonia Management: A Scoping Review

Abstract

Antimicrobial resistance (AMR) is a major public health challenge globally, particularly in pneumonia where inappropriate antibiotic use is common, resulting in increased morbidity and mortality. Artificial intelligence (AI) and clinical decision support systems (CDSS) have emerged as key tools to enhance antimicrobial stewardship (AMS) practices and reduce AMR. This scoping review aims to present and map the current AI and real-time CDSS applications in AMS for pneumonia patients, focusing on their types used and associated outcomes. This scoping review was conducted according to Arksey and O’Malley methodological framework and reported according to the PRISMA-ScR checklist. Databases including PubMed, CINAHL, EMBASE, and Scopus, were searched between April and August 2025. Original studies published in English between 2015 and 2025 were included. Out of 505 identified articles, 11 eligible studies were analysed. The findings showed that AI and CDSS tools, when integrated with machine learning (ML) algorithms and large databases, enhance diagnostic accuracy, optimise antibiotic use, improve pathogen identification, enhance AMR detection, promote guideline adherence, and support treatment-related decisions, thereby reducing mortality, healthcare costs, and the overuse of broad-spectrum antibiotics. However, integrating these technologies into clinical workflows remains a challenge due to limited research in low- and middle-income countries, data quality issues, and associated ethical concerns. AI and the CDSS are promising technologies to enhance AMS, especially in pneumonia, with improved patient outcomes. Future research to validate these technologies in diverse settings, while addressing barriers to their implementation and ethical concerns, is needed to enhance AMS practices and reduce AMR globally.

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