The role of Artificial Intelligence models in clinical decision support for infectious disease diagnosis and personalized treatment planning

Artificial intelligence is revolutionizing infectious disease management through innovative approaches to diagnosis, treatment optimization, and epidemiological surveillance. This systematic review examines AI applications in clinical decision support systems, evaluating their implementation across diverse healthcare settings while identifying critical adoption barriers. Recent advancements demonstrate remarkable success in rapid pathogen identification, early warning systems for conditions like sepsis, and personalized antimicrobial selection based on local resistance patterns. Despite these promising developments, significant challenges persist in translating AI solutions into clinical practice, including data quality issues, implementation barriers, and ethical concerns regarding algorithmic fairness and global health equity. Looking forward, explainable AI architectures, federated learning approaches, and treatment simulation through digital twins show potential for transforming care delivery, particularly in resource-limited settings. We propose targeted recommendations across three domains: standardized validation methodologies, comprehensive stakeholder engagement strategies, and equity-centered development frameworks. Successful integration requires coordinated efforts among healthcare organizations, researchers, policymakers, and clinicians to ensure AI enhances rather than complicates clinical decision-making. With appropriate attention to technical rigor, implementation science, and ethical considerations, AI-based systems can become valuable tools in combating infectious diseases while optimizing resource utilization.