Objective:

To analyze global research trends and future directions in the application of transformer techniques for image-driven cancer diagnosis from 2017 to 2026.

Key Findings:

• Rapid increase in publications on transformer applications in image-driven cancer diagnosis, particularly since 2022.
• China and the United States are the leading contributors with significant international collaboration emphasized.
• Research primarily focuses on transformer-based models for image classification, segmentation, and enhancement.
• Emerging trends include lightweight design, interpretability, multimodal fusion, and low annotation dependence.
• Citation impact varies across countries and institutions despite increasing publication volume.

Interpretation:

The field of transformer applications in cancer diagnosis is experiencing robust growth, highlighting the need for further research to enhance clinical applicability and generalizability of findings.

Limitations:

• Variability in citation impact across different countries and institutions.
• Challenges in generalizability and scalability of research findings.
• Lack of comprehensive bibliometric analysis in the field.

Conclusion:

The study highlights the promising growth of transformer-enhanced techniques in cancer diagnosis, emphasizing the importance of translating research into clinical practice while addressing challenges in generalizability and scalability.