Artificial Intelligence in Infectious Diseases: Current Applications, Challenges, and Future Directions

Importance: Infectious diseases remain a major global health threat, compounded by antimicrobial resistance, delayed diagnoses, and periodic outbreaks. Artificial intelligence (AI) offers transformative potential to enhance clinical decision-making, optimize antimicrobial use, and strengthen surveillance. Objective: To review current applications of AI in infectious diseases and outline key challenges and future directions. Observations: AI-based tools are being applied in diverse domains. Machine learning algorithms improve diagnostic accuracy in tuberculosis, sepsis, and pneumonia. Natural language processing supports antimicrobial stewardship by analyzing electronic health records. Predictive analytics models anticipate disease outbreaks by integrating epidemiological and environmental data. Additionally, AI-guided drug discovery accelerates identification of new antimicrobials. Despite promise, challenges remain, including data bias, lack of transparency in “black-box” models, infrastructural limitations, and ethical concerns regarding patient privacy and equity. Conclusions and Relevance: AI has demonstrated significant value in infectious disease care, from bedside clinical support to global outbreak prediction. However, successful translation requires high-quality data, explainable models, interdisciplinary collaboration, and equitable deployment, especially in low- and middle-income countries.