Bibliometric Trends in the Integration of Computer Vision With Healthcare.

Area of Science:

• Computer vision
• Machine learning
• Medical imaging analysis

Background:

• Computer vision, powered by machine learning, is vital for analyzing medical images (MRI, CT, X-rays) in healthcare.
• Applications include disease diagnosis, treatment planning, telemedicine, and remote patient monitoring.
• Existing research is fragmented, hindering a comprehensive understanding of the field's evolution and patterns.

Purpose of the Study:

• To systematically analyze global research trends in computer vision applications within healthcare.
• To provide a comprehensive overview of the intellectual structure, research hotspots, and collaborative patterns.
• To identify future research directions.

Main Methods:

• Bibliometric analysis of 1023 publications from the Web of Science (2000-2022).
• Analysis of scientific output, citations, journals, authors, institutions, countries, and funding agencies.
• Utilized VOSviewer, Biblioshiny, and ScientoPy for data analysis.

Main Results:

• Consistent upward trend in scientific output, peaking in 2021.
• IEEE Access was the most productive journal; IEEE Transactions on Medical Imaging was the most cited.
• China led in publications and collaboration, followed by the US.
• Dominant themes included deep learning, image segmentation, classification, and medical imaging.

Conclusions:

• The study offers a comprehensive overview of computer vision in healthcare research.
• Findings provide valuable insights for researchers, clinicians, and policymakers.
• Identified key research hotspots and collaborative patterns to inform future investigations.