Mapping the knowledge landscape and research trends of artificial intelligence in breast cancer diagnosis and treatment: A bibliometric analysis.
TL;DR
AI research in breast cancer has expanded rapidly with evolving themes, yet a translational gap persists between innovation and clinical integration, and future efforts should prioritize prospective validation, international collaboration with underrepresented regions, and standardized frameworks for integrating explainable and privacy-preserving AI into workflows.
Abstract
BackgroundArtificial intelligence (AI) has generated rapidly growing research in breast cancer diagnosis and treatment, yet its intellectual structure, collaboration patterns, and thematic evolution remain unmapped.ObjectiveTo analyze the global research landscape of AI in breast cancer from 2001 to 2025, identifying publication trends, contributors, collaboration networks, thematic clusters, and translational gaps.MethodsA bibliometric analysis of 7673 Web of Science articles. VOSviewer was used for co-authorship networks and keyword co-occurrence with overlay visualization; CiteSpace for burst detection.ResultsThe field grew exponentially (29.20% annual growth), with 88.41% of publications from 2020-2025. China (31.63%) and the United States (21.10%) dominated output, but co-authorship networks revealed limited international collaboration for China (22.4% non-Chinese co-authors) and structural exclusion of low- and middle-income countries. Seven keyword clusters showed persistent separation between technology-centric and clinically oriented terms. Temporal overlay revealed a shift from computer-aided detection (pre-2015) to deep learning (2015-2022) and explainable AI, federated learning, and vision transformers (2022-2025). Burst detection confirmed "feature selection" (strength=19.53) and "computer aided detection" (strength=20.24) as historical hotspots; limited recent bursts indicate emerging frontiers are still accumulating citation impact.ConclusionsAI research in breast cancer has expanded rapidly with evolving themes, yet a translational gap persists between innovation and clinical integration. Future efforts should prioritize prospective validation, international collaboration with underrepresented regions, and standardized frameworks for integrating explainable and privacy-preserving AI into workflows.