Artificial intelligence in endoscopy and colonoscopy: a comprehensive bibliometric analysis of global research trends - Letafatkar, Negin; El-Sehrawy, Amr Ali Mohamed Abdelgawwad; Prasad, KDV; Alkhayyat, Ahmad; Amini-Salehi, Ehsan; Hasanpour, Maryam; Taleshani, Masoomeh Namdar; Hashemi, Mohammad; Alotaibi, Hadi; Rashidian, Pegah; Keivanlou, Mohammad-Hossein; Hassanipour, Soheil (Frontiers in Medicine, 2025)
BackgroundArtificial intelligence (AI) has revolutionized the field of gastroenterology, particularly in endoscopic and colonoscopic procedures. These AI technologies aim to enhance diagnostic accuracy by facilitating the detection of gastrointestinal lesions, such as polyps and neoplasms. However, the rapid expansion of research in this area necessitates a comprehensive analysis to assess global trends and contributions. This study aims to conduct a thorough bibliometric and visualization analysis of global research focused on AI applications in endoscopy and colonoscopy.MethodsA systematic search was conducted in September 2024 using the Web of Science Core Collection. The data were analyzed using VOSviewer, CiteSpace, and R software, focusing on co-authorship, co-citation, and keyword trends.ResultsResearch output on AI in endoscopy and colonoscopy has seen significant growth since 2016, peaking in 2023 with 345 publications. The top contributing country was China, with 399 publications, while the United States led in centrality with a score of 0.27, indicating its key position in research collaborations. Showa University contributed the highest number of institutional publications (64 papers). Mori Y emerged as the leading author, with 53 publications, reflecting his significant influence in the field. The leading journal was Gastrointestinal Endoscopy, contributing 72 publications and accumulating 6,496 citations. The most frequently occurring keywords were “diagnosis,” “classification,” and “cancer.” The cluster analysis identified key research areas, with newer clusters emerging around “adenoma detection,” “polyp segmentation,” and “wireless capsule endoscopy.” These clusters have shown an increasing trend over the past few years, reflecting the growing focus on using AI to optimize diagnostic procedures in real-time.ConclusionThe bibliometric analysis highlights the rapid expansion and diversification of AI research in endoscopy and colonoscopy. Key clusters, such as “adenoma detection” and “polyp segmentation,” underscore the field's shift toward real-time diagnostic improvements. As AI technologies become more integrated into clinical practice, they are set to improve diagnostic accuracy and patient outcomes in gastroenterology.