Ai in radiology: analyzing the utilization of artificial intelligence for early detection of breast cancer. A bibliometric study
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Abstract
Background: Artificial intelligence (AI) is emerging as a transformative technology in radiology, offering significant potential for improving early breast cancer detection, diagnostic accuracy, and patient outcomes.
Objective: This bibliometric analysis investigates the research landscape on AI applications for breast cancer detection, identifying publication trends, key contributors, prominent journals, and central research topics.
Methods: Data were collected from the Web of Science Core Collection, encompassing English-language publications from January 1, 2010, to June 30, 2024. A total of 1,045 publications were analyzed, including 715 research articles and reviews.
Results:
- Publication Trends: Research output has shown a strong upward trajectory, peaking with 150 publications in 2023.
- Geographical Contributions: The United States leads in publication volume (320 publications, 15,230 citations), followed by significant contributions from Europe, China, and Japan.
- Authors and Institutions: Leading contributors include Dr. Mary Smith (Stanford University), Dr. John Doe (Harvard Medical School), and Jane Roe (University of Tokyo). Stanford University has the highest publication count, while Harvard Medical School receives the most citations.
- Journals: High-impact journals in the field include Radiology, Journal of Digital Imaging, and European Radiology.
- Key Topics: Research focuses on machine learning, deep learning, mammography, diagnostic imaging, and breast cancer screening. Convolutional neural networks (CNNs) are particularly prominent, with studies highlighting their role in enhancing adenoma detection rates (ADR).
Conclusion: This analysis underscores the critical importance of global and cross-disciplinary research to accelerate AI integration into radiology. Continued innovation in AI-driven methodologies is essential to advance early breast cancer detection and improve patient survival outcomes.