Harnessing AI for advancing pathogenic microbiology: a bibliometric and topic modeling approach

Area of Science:

• Pathogenic Microbiology
• Computational Biology
• Infectious Disease Research

Background:

• Artificial intelligence (AI) integration is accelerating research and innovation in pathogenic microbiology.
• Understanding the evolution and trends of AI applications is crucial for advancing the field.

Purpose of the Study:

• To explore the evolution and trends of AI applications in pathogenic microbiology.
• To provide insights into how AI is transforming research and practice in this domain.

Main Methods:

• Bibliometric analysis and topic modeling of 27,420 publications (2010-2024) from Web of Science Core Collection.
• Identification of key trends, research areas, and geographical distribution of AI in pathogenic microbiology research.

Main Results:

• Exponential increase in AI publications since 2016, with major contributions from China and the USA.
• Eight key AI application areas identified: pathogen detection, antibiotic resistance prediction, transmission modeling, genomic analysis, therapeutic optimization, ecological profiling, vaccine development, and data management.
• Significant lexical overlaps noted between research areas, particularly drug resistance and vaccine development.

Conclusions:

• AI is transitioning from laboratory research to clinical applications, enhancing hospital operations and public health.
• AI optimizes pathogen detection, diagnostic speed, treatment efficacy, and disease control, including rapid antibiotic susceptibility testing and vaccine development.
• The study provides a roadmap for future AI research, resource allocation, and policy-making in pathogenic microbiology.