Artificial Intelligence in Infectious Disease Care: Selected Applications in Tuberculosis, Sepsis, and Antimicrobial Stewardship.

Background/

Objectives

Artificial intelligence (AI) is increasingly being applied across the infectious-disease pathway, from syndromic surveillance and imaging triage to etiologic support, antimicrobial stewardship, and prognostication. However, the maturity of evidence differs considerably across use cases, and apparent technical performance does not always translate into real-world clinical utility.

Methods

This structured narrative review synthesizes current evidence on the principal clinical and public-health applications of AI in infectious diseases, with particular attention to external validation, workflow integration, economic implications, and governance.

Results

The strongest near-term evidence supports narrow-AI applications linked to constrained workflows, especially tuberculosis chest-radiograph triage, selected host-response and antimicrobial-resistance prediction tools, and clinician-facing stewardship aids. By contrast, sepsis prediction illustrates how internal model performance may deteriorate on external validation and generate substantial alert burden when implemented in routine care. Economic evaluations are promising but remain predominantly model-based and context-dependent. Evidence for generative AI and large language models is still in an early phase, consisting largely of vignette studies, retrospective comparisons, and small single-center pilots rather than prospective outcome-based evaluations.

Conclusions

Overall, the most realistic clinical role of AI in infectious diseases is augmentation rather than replacement: prioritizing scarce diagnostic capacity, shortening time to action, and improving antibiotic selection. Safe translation into practice requires, in order, external validation with local calibration, prospective impact assessment, and governance frameworks that address drift, accountability, transparency, and human oversight.