Objective To understand the application of artificial intelligence (AI) in research of neglected tropical diseases (NTDs) in the world.

Methods We retrieved all the English-language publications on AI applications in research of NTDs during 2005-2024 from the core database of Web of Science, and analyzed their publication years, subject categories, journals, and keywords. By using "ggalluvial" and "ggplot2" packages, a spatial flow Sankey diagram was created to depict the flow of collaboration among countries, institutions and authors, and research outputs.

Results A total of 694 articles were included. The number of annual publications about the application of AI in NTDs research showed an overall upward trend from 2005 to 2024. The three most influential research areas were parasitology, tropical medicine, and infectious diseases, which significantly contributed to the growth in total publications. PLoS Neglected Tropical Diseases was the journal with the highest publications and citations in this field. The United States, China, and India were core countries applying AI in research of NTDs , with institutions such as Harvard University, University of London, and the Chinese Academy of Sciences leading transnational collaborative networks. The research exhibits a "core-periphery" collaboration pattern, and high-productivity authors predominantly belonged to multinational teams led by European and American institutions. Key research outputs focued on AI-assisted drug discovery, disease transmission prediction, and diagnostic technology innovation. Co-citation clustering analysis on the literature revealed that the research field covered hot topics such as disease prediction and vector surveillance, medical imaging and diagnostic assistance, drug discovery and mechanism research, public health and health management, as well as model optimization and interdisciplinary integration. High-frequency keywords included machine learning, dengue, infection, aedes aegypti, deep learning and transmission. Research on AI applications in NTDs has evolved from preliminary exploration to integrated analysis and management through multi-source data fusion.

Conclusion AI application in research of NTDs has demonstrated consistent growth, with research focus shifting from early algorithm development to multi-source data integration. However, significant challenges still persist, including geographical disparities in research collaboration and insufficient technological translation. Enhanced international cooperation and accessible technology development are imperative to facilitate practical implementation.