Mapping the landscape of AI and ML in vaccine innovation: A bibliometric study.

Abstract

With the rapid advancement of artificial intelligence (AI) and machine learning (ML) technologies, their applications in the medical field have expanded significantly. Particularly in vaccine innovation, AI and ML have shown considerable potential. This article employs bibliometric analysis to examine the progress of AI and ML in vaccine innovation over recent years. By conducting literature retrieval, data extraction, and intelligent analysis through Web of Science, it provides more accurate and comprehensive insights into vaccine development and dosimetry. The rapid growth in research publications since 2012, particularly the geometric growth observed since 2017, underscores the increasing recognition of the potential of AI and ML to revolutionize vaccine development. However, despite the substantial benefits of AI and ML in vaccine innovation, challenges remain regarding data quality, algorithm reliability, and ethical considerations. As technology continues to advance and research deepens, AI and machine learning are anticipated to play an even more pivotal role in vaccine innovation. Notably, AI has the potential to accelerate vaccine development timelines, particularly in the context of emerging infectious diseases. By leveraging data-driven insights and predictive modeling, AI can streamline processes such as antigen discovery, clinical trial design, and risk assessment, thereby enabling faster responses to public health emergencies. This capability is especially critical for addressing sudden outbreaks of infectious diseases, where rapid deployment of effective vaccines can significantly mitigate global health risks.