Emerging infectious diseases are one of the most significant threats to global health, driven by many factors such as zoonotic spillovers, climate change, globalization, and antibiotic resistance. While a great deal of attention is focused on viral and bacterial pathogens (e.g., SARS-CoV-2, influenza, multidrug-resistant TB), parasitic diseases contribute to global morbidity and mortality that remain largely unrecognized. The recent development of artificial intelligence has introduced powerful computational tools that can integrate large and complex datasets to assist with infectious disease surveillance, diagnosis, outbreak prediction, and drug discovery. Artificial intelligence encompasses machine learning, deep learning, and natural language processing techniques, which allow for automated pattern recognition and predictive modeling based on very complex biomedical data sets. This narrative review explores the recent advancements in AI applications in four key areas related to infectious disease: disease surveillance and early-warning systems; diagnostics and clinical decision support; outbreak prediction and modeling; and drug/vaccine discovery. Emphasis will be placed on applications of AI to parasites such as malaria, leishmaniasis, and soil-transmitted helminths. In addition, we discuss several challenges related to AI implementation in endemic regions including limited data availability, algorithmic bias, limited infrastructure in endemic areas, and ethical issues regarding data governance. Integrating AI into the One Health framework of linking human, animal, and environmental health will potentially enhance global preparedness to respond to emerging infectious and parasitic diseases.
Nasr et al. (2026) studied this question.