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<p>Ivermectin, a widely used antiparasitic drug, gained global attention during the COVID-19 pandemic for its proposed antiviral effects. This study explores how ivermectin research evolved between 2020 and 2025, identifying key trends, emerging topics, and thematic changes. A data-driven bibliometric and informatics-based analysis was conducted using the Scopus database. Original articles were examined through advanced co-citation, co-word, and bibliographic coupling techniques. We employed artificial intelligence–enabled clustering algorithms, including Walktrap and hierarchical models, using tools such as VOSviewer and Bibliometrix (R package), to explore the conceptual landscape and research dynamics. A total of 1,041 ivermectin-related articles were included. Research topics shifted from antiparasitic and veterinary uses to newer areas like COVID-19 treatment, drug repurposing, oxidative stress, and vector control. We identified 12 thematic clusters and six co-word clusters. Although citation impact declined after 2021, interest remained strong in molecular and pharmacological mechanisms. Key authors, journals, and studies were also identified. This is the first AI-enhanced bibliometric mapping of ivermectin literature in the post-pandemic era. The findings uncover underexplored but promising research directions—such as apoptosis, immunomodulation, and nanodelivery—and highlight the added value of machine learning tools in uncovering complex thematic transitions and research frontiers. This approach offers deeper insights than traditional bibliometric methods by uncovering nuanced thematic transitions through AI-powered clustering.</p>

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Global research hotspots, trends, and future directions of ivermectin research: an informatics investigation (2020–2025)

  • 期刊论文

  • 发表日期：

    2025年8月14日

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  • Siddig Ibrahim Abdelwahab,
  • Manal Mohamed Elhassan Taha,
  • Khaled A. Sahli,
  • Moath Mohamed Farasani,
  • Marwa Qadri,
  • Abdulaziz Alarifi,
  • Amani Khardali,
  • Khloud H. Alsaadi,
  • Abdullah Farasani,
  • Nizar A. Khamjan,
  • Jobran M. Moshi,
  • Saeed Alshahrani,
  • Ahmad Assiri

摘要

Ivermectin, a widely used antiparasitic drug, gained global attention during the COVID-19 pandemic for its proposed antiviral effects. This study explores how ivermectin research evolved between 2020 and 2025, identifying key trends, emerging topics, and thematic changes. A data-driven bibliometric and informatics-based analysis was conducted using the Scopus database. Original articles were examined through advanced co-citation, co-word, and bibliographic coupling techniques. We employed artificial intelligence–enabled clustering algorithms, including Walktrap and hierarchical models, using tools such as VOSviewer and Bibliometrix (R package), to explore the conceptual landscape and research dynamics. A total of 1,041 ivermectin-related articles were included. Research topics shifted from antiparasitic and veterinary uses to newer areas like COVID-19 treatment, drug repurposing, oxidative stress, and vector control. We identified 12 thematic clusters and six co-word clusters. Although citation impact declined after 2021, interest remained strong in molecular and pharmacological mechanisms. Key authors, journals, and studies were also identified. This is the first AI-enhanced bibliometric mapping of ivermectin literature in the post-pandemic era. The findings uncover underexplored but promising research directions—such as apoptosis, immunomodulation, and nanodelivery—and highlight the added value of machine learning tools in uncovering complex thematic transitions and research frontiers. This approach offers deeper insights than traditional bibliometric methods by uncovering nuanced thematic transitions through AI-powered clustering.