Artificial Intelligence in Infectious Disease Research.

Abstract

Background: Infectious diseases remain a major global health challenge, and while AI is rapidly advancing diagnostic techniques such as Computed Tomography (CT), Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI), and ultrasound, its full potential in the detection and management of these diseases has yet to be thoroughly explored. This study examines the global research trends, key contributors, and thematic shifts in the field of AI-driven diagnostics for infectious diseases.

Methods: The publications obtained data from the Web of Science (WoS) database reveal an exponential growth with 5465 publications from 2005 to 2024. The search targeted studies on human, animal, and environmental health using keywords such as "infectious disease," "communicable disease," "environment," "machine learning," and "artificial intelligence" in title, abstract, and keyword fields. Bibliometric parameters such as publication trends, prolific authors, and geographical distribution were assessed, and core journals, institutions were identified, keyword co-occurrence analysis highlighted major research themes. Analyses were conducted using pyBibX, bibliometrix, and VOSviewer to map relationships among authors, journals, and thematic areas.

Findings: The USA leads AI-driven infectious disease research, contributing 1687 publications and 72,732 citations, dominated the combined contributions of China and India, whose total amounts to 1768 publications and 34,927 citations. The Chinese Academy of Science (China), University of Oxford (UK), and Harvard Medical School (USA) emerged as the top institutions. "Scientific Reports" turned out to be the top journal with 157 publications followed by "PLOS One" (144) and "Frontiers in Immunology" (127). Citation network analysis highlighted regional disparities, with low-income countries such as Gambia and Mozambique underrepresented. Keyword clustering identified six key research themes, with machine learning as a central theme across diagnostics, epidemiology, imaging, and drug discovery.

Conclusion: AI is transforming infectious disease research with significant contributions from high-income countries. However, emerging collaborations in lower middle-income countries highlight the need for equitable AI adoption. Addressing these disparities in infrastructure, funding, technological, and computational capability through global collaborations is important. Future studies should focus on overcoming the real-world implementation challenges to maximize AI's impact in infectious disease research.