Title : Artificial intelligence–driven clinical decision support for differentiating viral and bacterial infections to reduce antibiotic misuse
Abstract:
Artificial intelligence (AI) has emerged as a powerful tool in modern healthcare, particularly in improving diagnostic accuracy and clinical decision-making. One of the major challenges in infectious disease management is the differentiation between viral and bacterial infections. Misdiagnosis often leads to unnecessary antibiotic prescriptions, contributing to the global crisis of antimicrobial resistance.
This study explores the potential of AI-driven clinical decision support systems (CDSS) to assist healthcare professionals in distinguishing viral from bacterial infections using patient clinical data, laboratory parameters, and predictive algorithms. Machine learning models can analyse large datasets to identify patterns and biomarkers associated with specific types of infections, enabling more precise and timely diagnosis.
The implementation of AI-based diagnostic tools in clinical settings can significantly reduce inappropriate antibiotic use, improve treatment outcomes, and support antimicrobial stewardship initiatives. Additionally, such systems can aid physicians in resource-limited settings by providing evidence-based recommendations and reducing diagnostic uncertainty.
The integration of artificial intelligence with clinical diagnostics represents a promising approach to addressing the growing burden of antimicrobial resistance worldwide. By improving diagnostic accuracy and guiding appropriate treatment strategies, AI-driven decision support systems have the potential to transform infectious disease management and enhance global public health outcomes.
