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<p>This study aims to conduct a comprehensive bibliometric analysis to evaluate the scope and progression of Artificial Intelligence (AI) and Machine Learning (ML) applications in Chronic Rhinosinusitis (CRS) within the field of otorhinolaryngology. Specifically, the objective is to analyse publication trends, collaboration networks, and thematic focus areas in the relevant literature, providing insights into the current state and future directions of AI and ML in managing CRS. A comprehensive literature search was conducted in Scopus and PubMed databases for studies published between 1987 and 2024, focusing on AI and ML in CRS. The bibliometrix package in R was utilized for data analysis, including performance assessment, science mapping, and network analysis to identify key contributors, collaboration patterns, and emerging themes within the research landscape. The analysis of 102 documents shows a consistent annual publication growth of 5.78%, with an average citation rate of 13.41 per document. The International Forum of Allergy and Rhinology was the most prolific source. Leading countries in scientific production were the USA, China, and South Korea. Thematic analysis highlighted a focus on diagnostic imaging, personalized treatment, and AI integration for improved decision-making in CRS management. Collaboration networks demonstrated strong international cooperation among diverse researchers and institutions. The bibliometric analysis highlights AI and ML’s growing impact on CRS research and practice. Increased publications and global collaborations indicate a strong research community leveraging AI for better outcomes. Despite challenges like data diversity, AI integration promises personalized medicine and improved patient care.</p>

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Artificial Intelligence and Machine Learning Applications in Chronic Rhinosinusitis: A Bibliometric Analysis

  • 期刊论文

  • 发表日期：

    2026年5月19日

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  • Vinothini Jayaraj,
  • Sridevi Gnanasekaran,
  • V. B. Yazhini,
  • Palani Selvam Mohanraj,
  • Geetha Mohan,
  • Vinoth Rajendran

摘要

This study aims to conduct a comprehensive bibliometric analysis to evaluate the scope and progression of Artificial Intelligence (AI) and Machine Learning (ML) applications in Chronic Rhinosinusitis (CRS) within the field of otorhinolaryngology. Specifically, the objective is to analyse publication trends, collaboration networks, and thematic focus areas in the relevant literature, providing insights into the current state and future directions of AI and ML in managing CRS. A comprehensive literature search was conducted in Scopus and PubMed databases for studies published between 1987 and 2024, focusing on AI and ML in CRS. The bibliometrix package in R was utilized for data analysis, including performance assessment, science mapping, and network analysis to identify key contributors, collaboration patterns, and emerging themes within the research landscape. The analysis of 102 documents shows a consistent annual publication growth of 5.78%, with an average citation rate of 13.41 per document. The International Forum of Allergy and Rhinology was the most prolific source. Leading countries in scientific production were the USA, China, and South Korea. Thematic analysis highlighted a focus on diagnostic imaging, personalized treatment, and AI integration for improved decision-making in CRS management. Collaboration networks demonstrated strong international cooperation among diverse researchers and institutions. The bibliometric analysis highlights AI and ML’s growing impact on CRS research and practice. Increased publications and global collaborations indicate a strong research community leveraging AI for better outcomes. Despite challenges like data diversity, AI integration promises personalized medicine and improved patient care.