Applying transformer-based deep learning models in image-driven cancer diagnosis: a comprehensive bibliometric analysis of global research trends

Background/objectives The transformer, introduced in 2017, has revolutionized several fields, including oncology. Its applications in cancer detection, diagnosis, prognosis prediction and treatment have shown significant potential. However, a comprehensive analysis of the global research trends and future directions in image-driven cancer diagnosis is lacking. Methods We conducted a bibliometric analysis using CiteSpace and VOSviewer to explore the development of transformer applications in image-driven cancer diagnosis(TICD). A total of 2923 papers published between 2017 and 2026 were analyzed, with early access papers from 2026 included. We examined publication trends, international collaboration, and citation patterns to identify research hotspots and emerging directions. Results The number of publications on transformer applications in image-driven cancer diagnosis has rapidly increased, with a notable surge beginning in 2022. China and the United States are the leading contributors to the field, with high levels of international collaboration. The primary research focuses on the application of transformer-based models for image classification, segmentation, and enhancement. Their development is moving toward lightweight design, interpretability, multimodal fusion, and low annotation dependence. However, while the volume of publications is increasing, the impact (measured by citation counts) varies across countries and institutions. Conclusions The field of TICD is in a robust growth phase, attracting significant attention from global researchers, particularly from China and the United states. While international collaboration is prevalent, the field faces challenges regarding the generalizability and scalability of research findings. Future research should focus on translating these promising technologies into clinical practice, ensuring that they are adaptable and applicable in diverse oncology contexts.