Emerging Trends and Key Themes in Deep Learning for Lung Cancer: A Bibliometric Analysis from 2014 to 2025
Keywords:
Deep Learning, Lung Cancer, Bibliometric AnalysisAbstract
This study investigates the application of deep learning (DL) techniques in lung cancer diagnostics through a comprehensive bibliometric analysis spanning 2014 to 2025. By analyzing 2,529 publications from 868 sources, the research identifies key trends, thematic advancements, and collaborative networks in the field. Central themes such as "deep learning," "lung cancer," and "medical imaging" are highlighted, with emerging topics like "transfer learning" and "bioinformatics" reflecting the evolution toward more sophisticated methodologies. While advancements in convolutional neural networks (CNNs) and explainable AI frameworks demonstrate potential for enhanced diagnostic precision, challenges persist, including dataset variability, model interpretability, and equitable access to AI technologies. Collaboration networks emphasize the role of global partnerships, though disparities in resource distribution and research activity remain apparent. This study underscores the importance of integrating computational and clinical perspectives to advance DL-driven lung cancer diagnostics, advocating for inclusive, cost-effective solutions to bridge existing gaps. The findings provide actionable insights for researchers, clinicians, and policymakers, fostering the development of AI-based tools to improve early detection, personalized treatment, and equitable healthcare access worldwide.
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