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SYSTEMATIC REVIEW article

Front. Microbiol.
Sec. Systems Microbiology
Volume 15 - 2024 | doi: 10.3389/fmicb.2024.1510139
This article is part of the Research Topic Artificial Intelligence in Pathogenic Microorganism Research View all 9 articles

Harnessing AI for Advancing Pathogenic Microbiology: A Bibliometric and Topic Modeling Approach

Provisionally accepted
Tian Tian Tian Tian 1Xuan Zhang Xuan Zhang 1*Fei Zhang Fei Zhang 1Xinghe Huang Xinghe Huang 2*Ming-Lin Li Ming-Lin Li 1Ziwei Quan Ziwei Quan 1*Wenyue Wang Wenyue Wang 1*Jiawei Lei Jiawei Lei 1*Yuting Wang Yuting Wang 1*Ying Liu Ying Liu 1*Jia-He Wang Jia-He Wang 1*
  • 1 China Medical University, Shenyang, China
  • 2 China Jiliang University, Hangzhou, Zhejiang Province, China

The final, formatted version of the article will be published soon.

    This study employs bibliometrics and topic modeling to conduct a comprehensive and in-depth analysis of artificial intelligence (AI) applications in pathogenic microbiology . Analyzing 27,420 publications from the Web of Science Core Collection (2010-2024) , we reveal an exponential increase in publications since 2016 , with China and the USA leading global research efforts . Our analysis identifies eight key AI application areas: pathogen detection, antibiotic resistance prediction, transmission modeling, genomic analysis, therapeutic optimization, ecological profiling, vaccine development, and data management systems . Significant lexical overlaps were found between research areas, particularly between drug resistance and vaccine development, highlighting an interconnected research framework . AI applications are transitioning from laboratory settings to clinical practice, optimizing hospital operations in pathogen detection and playing critical roles in public health , significantly enhancing diagnostic speed, treatment efficacy, and disease control strategies, particularly in rapid antibiotic susceptibility testing and COVID-19 vaccine development . This study comprehensively illuminates the current research status, advancements, and challenges in the field, providing a foundation for future resource allocation and policy-making.

    Keywords: Pathogenic microorganisms, Artificial intelligence (AI), Machine Learning (ML), deep learning (DL), Bibliometrics, Topic Modeling, Antimicrobial resistance (AMR)

    Received: 12 Oct 2024; Accepted: 06 Nov 2024.

    Copyright: © 2024 Tian, Zhang, Zhang, Huang, Li, Quan, Wang, Lei, Wang, Liu and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Xuan Zhang, China Medical University, Shenyang, China
    Xinghe Huang, China Jiliang University, Hangzhou, 310018, Zhejiang Province, China
    Ziwei Quan, China Medical University, Shenyang, China
    Wenyue Wang, China Medical University, Shenyang, China
    Jiawei Lei, China Medical University, Shenyang, China
    Yuting Wang, China Medical University, Shenyang, China
    Ying Liu, China Medical University, Shenyang, China
    Jia-He Wang, China Medical University, Shenyang, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.