From bedside queries to complex care: AI for decision-making in infectious diseases

Infectious Diseases require rapid decisions based on heterogeneous and evolving clinical data. At the same time, the shortage of infectious diseases specialists continues to widen the gap between demand for expertise and its availability. Artificial intelligence (AI) has emerged as a potential support tool, although its role in routine care remains uncertain.

To review current and emerging applications of AI in infectious disease decision-making and to examine their clinical relevance, limitations, and implementation challenges.

Recent literature was identified through searches of PubMed/MEDLINE, Scopus, Web of Science and Google Scholar, complemented by manual screening of references from relevant articles on AI applications in infectious diseases.

AI applications in infectious diseases span three main areas of clinical decision-making. Large language models can assist with knowledge retrieval and support routine diagnostic and management questions. Machine learning models are primarily used for risk stratification, identifying patients at risk of clinical deterioration, antimicrobial resistance, or infection recurrence, with potential implications for triage and early intervention. AI-supported therapeutic tools focus on empiric antibiotic selection, antimicrobial stewardship, and dose optimization.

AI is best viewed as a decision-support layer that may extend specialist expertise and improve consistency of care. Its clinical adoption, however, depends on external validation across settings, integration within existing workflows, and continued clinician oversight.