ReviewDigital health
From pharmacometric foundations to emerging artificial intelligence applications: A bibliometric analysis of model-informed precision dosing for anti-infective therapy (2005-2025).
Review in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: This study aims to provide a comprehensive bibliometric analysis of model-informed precision dosing (MIPD) research in anti-infective therapy from 2005 to 2025, systematically mapping its evolution from pharmacometric foundations to emerging artificial intelligence applications, and identifying key research trends, institutional contributions, and future research priorities in this rapidly evolving field. Methods: The terminology related to MIPD and anti-infective drugs was used as a search strategy to retrieved literature from the core collection of Web of Science, and the data obtained were analyzed by VOSviewer, CiteSpace and Bibliometrix. Results: A total of 4,601 articles were included, the USA published the largest number of documents, Australia had the highest citations per publication. The University of Queensland was the most productive institution. Roberts JA and Lipman J were the most published authors. The field underwent a foundational phase of developing population pharmacokinetic models, a consolidation phase aimed at promoting Bayesian-guided TDM and extending it to special populations, and a frontier phase of parallel advancement involving traditional MIPD and emerging machine learning frameworks. Critically, the divergence between pharmacokinetic metrics used in traditional MIPD and predictive accuracy metrics favored by machine learning approaches represents a principal obstacle to clinical integration. Conclusion: This study provides the first comprehensive bibliometric map of two decades of MIPD research in anti-infective therapy, documenting its evolution from pharmacometric foundations to emerging AI-assisted applications, and identifies prospective clinical validation, EHR integration, and harmonized ML-MIPD evaluation frameworks as key future priorities.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.