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<p>Radiology has been profoundly transformed by artificial intelligence (AI) over the past decade, enabling automated detection, enhanced diagnostic accuracy, and more personalized patient care. In this study, we performed a&#xa0;bibliometric analysis of the scientific literature on AI in radiology from 2010, aiming to map research trends, identify influential authors and institutions, and uncover emerging thematic areas. Our results show a&#xa0;rapid growth of publications since 2016, with the United States, China, and Germany leading in output. Journals such as the <i>European Journal of Radiology, Academic Radiology</i>, and <i>Clinical Radiology</i> have been the most productive, while keyword analysis revealed emerging topics including explainable AI, multimodal imaging, and AI-assisted clinical decision-making. Collaborative networks among countries and institutions have expanded, reflecting increasing international cooperation. This bibliometric overview highlights the evolving landscape of AI in radiology and provides insights to guide future research and clinical integration.</p>

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Exploring AI in radiology: a bibliometric study tailored to a research path

  • 期刊论文

  • 发表日期：

    2026年3月3日

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  • Ikram Gouri,
  • Rachid Dehbi,
  • Amine Dehbi,
  • Zouhair Ibn Batouta

摘要

Radiology has been profoundly transformed by artificial intelligence (AI) over the past decade, enabling automated detection, enhanced diagnostic accuracy, and more personalized patient care. In this study, we performed a bibliometric analysis of the scientific literature on AI in radiology from 2010, aiming to map research trends, identify influential authors and institutions, and uncover emerging thematic areas. Our results show a rapid growth of publications since 2016, with the United States, China, and Germany leading in output. Journals such as the European Journal of Radiology, Academic Radiology, and Clinical Radiology have been the most productive, while keyword analysis revealed emerging topics including explainable AI, multimodal imaging, and AI-assisted clinical decision-making. Collaborative networks among countries and institutions have expanded, reflecting increasing international cooperation. This bibliometric overview highlights the evolving landscape of AI in radiology and provides insights to guide future research and clinical integration.