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Application of Artificial Intelligence in the diagnosis and treatment of colorectal cancer: a bibliometric analysis, 2004–2023

2024·11 Zitationen·Frontiers in OncologyOpen Access
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11

Zitationen

5

Autoren

2024

Jahr

Abstract

Background: An increasing number of studies have turned their lens to the application of Artificial Intelligence (AI) in the diagnosis and treatment of colorectal cancer (CRC). Objective: To clarify and visualize the basic situation, research hotspots, and development trends of AI in the diagnosis and treatment of CRC, and provide clues for research in the future. Methods: On January 31, 2024, the Web of Science Core Collection (WoSCC) database was searched to screen and export the relevant research published during 2004-2023, and Cite Space, VoSviewer, Bibliometrix were used to visualize the number of publications, countries (regions), institutions, journals, authors, citations, keywords, etc. Results: A total of 2715 pieces of literature were included. The number of publications grew slowly until the end of 2016, but rapidly after 2017, till to the peak of 798 in 2023. A total of 92 countries, 3997 organizations, and 15,667 authors were involved in this research. Chinese scholars released the highest number of publications, and the U.S. contributed the highest number of total citations. As to authors, MORI, YUICHI had the highest number of publications, and WANG, PU had the highest number of total citations. According to the analysis of citations and keywords, the current research hotspots are mainly related to "Colonoscopy", "Polyp Segmentation", "Digital Pathology", "Radiomics", "prognosis". Conclusion: Research on the application of AI in the diagnosis and treatment of CRC has made significant progress and is flourishing across the world. Current research hotspots include AI-assisted early screening and diagnosis, pathology, and staging, and prognosis assessment, and future research is predicted to put weight on multimodal data fusion, personalized treatment, and drug development.

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Autoren

Institutionen

Themen

Radiomics and Machine Learning in Medical ImagingArtificial Intelligence in Healthcare and EducationAI in cancer detection
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