AUTHOR=Tian Tian , Zhang Xuan , Zhang Fei , Huang Xinghe , Li Minglin , Quan Ziwei , Wang Wenyue , Lei Jiawei , Wang Yuting , Liu Ying , Wang Jia-He TITLE=Harnessing AI for advancing pathogenic microbiology: a bibliometric and topic modeling approach JOURNAL=Frontiers in Microbiology VOLUME=Volume 15 - 2024 YEAR=2024 URL=https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2024.1510139 DOI=10.3389/fmicb.2024.1510139 ISSN=1664-302X ABSTRACT=IntroductionThe integration of artificial intelligence (AI) in pathogenic microbiology has accelerated research and innovation. This study aims to explore the evolution and trends of AI applications in this domain, providing insights into how AI is transforming research and practice in pathogenic microbiology.MethodsWe employed bibliometric analysis and topic modeling to examine 27,420 publications from the Web of Science Core Collection, covering the period from 2010 to 2024. These methods enabled us to identify key trends, research areas, and the geographical distribution of research efforts.ResultsSince 2016, there has been an exponential increase in AI-related publications, with significant contributions from China and the USA. Our analysis identified eight major AI application areas: pathogen detection, antibiotic resistance prediction, transmission modeling, genomic analysis, therapeutic optimization, ecological profiling, vaccine development, and data management systems. Notably, we found significant lexical overlaps between these areas, especially between drug resistance and vaccine development, suggesting an interconnected research landscape.DiscussionAI is increasingly moving from laboratory research to clinical applications, enhancing hospital operations and public health strategies. It plays a vital role in optimizing pathogen detection, improving diagnostic speed, treatment efficacy, and disease control, particularly through advancements in rapid antibiotic susceptibility testing and COVID-19 vaccine development. This study highlights the current status, progress, and challenges of AI in pathogenic microbiology, guiding future research directions, resource allocation, and policy-making.