Objective:

To conduct a bibliometric analysis of the research landscape of model-informed precision dosing (MIPD) in anti-infective therapy from 2005 to 2025, highlighting trends and emerging applications of AI.

Approach:

• Data Source: Data was sourced from the Web of Science Core Collection, focusing on literature related to MIPD and anti-infective therapy.
• Search Strategy: A comprehensive search strategy was employed to identify relevant publications, resulting in 4,652 records for analysis after filtering.
• Data Analysis Tools: Bibliometric analysis was conducted using tools like VOSviewer, Bibliometrix, and CiteSpace to visualize knowledge structure and research trends.

Key Findings:

• The bibliometric analysis reveals a growing recognition of MIPD's role in optimizing dosing regimens amidst antibiotic resistance.
• The integration of AI and machine learning into MIPD is identified as a significant trend in precision dosing methodologies.
• The analysis shows a shift in research focus from theoretical modeling to practical validation and application of MIPD.

Interpretation:

The findings indicate a growing interest and evolution in the field of MIPD, particularly with the incorporation of AI technologies, which may enhance clinical decision-making in anti-infective therapy.

Limitations:

• The study is limited to publications in English and may not capture all relevant research in other languages.
• The analysis is confined to the Web of Science database, potentially excluding relevant studies from other sources.

Conclusion:

This bibliometric study offers a comprehensive overview of trends and developments in MIPD research, providing insights for future research directions.