ArticleHealth science reports2025
Utilization of Artificial Intelligence in Reducing the Incidence of Medication Error: A Bibliometric Analysis.
Article in Health science reports, 2025. 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.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Aims: Medication errors (MEs) represent a significant challenge in healthcare, compromising patient safety and contributing to adverse outcomes. Artificial intelligence (AI) has emerged as a promising tool to address this issue by enhancing medication management processes and decision support systems. This study aims to visualize and examine the dissemination of published work on AI-related research in reducing MEs. Methods: Data collected from the Scopus database was used for bibliometric analysis. One hundred eighty-four ( Results: The study revealed that most articles published were empirical, written by multiple authors from more developed nations, and published in medical-related journals. There has been a stable increase in publications since 1991, peaking in 2023, with several authors, organizations, and journals publishing more than others. Notable keywords such as "medication error", "artificial intelligence", and "patient safety" highlight central concepts explored in the research on AI and medication error reduction. The clustering analysis identified overarching themes, including providing insights into the Conclusion: Empirical research is crucial for understanding AI utilization in reducing MEs. The medical community is increasingly interested in using AI to mitigate MEs and address critical issues related to patient safety in medication administration. The identified prominent keywords and themes illustrate AI's potential in enhancing healthcare delivery and reducing mistakes, paving the way for further exploration and practical application in clinical settings. Additional studies on AI use in reducing MEs should be conducted in less developed countries.
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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.