ReviewHealthcare technology letters
Bibliometric Trends in the Integration of Computer Vision With Healthcare.
Review in Healthcare technology letters. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computer vision, enabled by machine learning and image processing techniques, plays a crucial role in healthcare by facilitating the analysis of medical images such as magnetic resonance imaging, computed tomography and X-rays. It supports disease diagnosis, treatment planning, telemedicine and remote patient monitoring. Despite rapid growth, research in this field remains fragmented, making it difficult to comprehensively understand its evolution and research patterns. This study aimed to systematically analyse global research trends in computer vision applications in healthcare using bibliometric methods. A bibliometric analysis was conducted on 1023 publications retrieved from the Web of Science (Wos) database published between 2000 and 2022. Scientific output, citation patterns, leading journals, authors, institutions, countries, funding agencies and research themes were analysed using VOSviewer, Biblioshiny and ScientoPy. The annual scientific output showed a consistent upward trend, with a peak in 2021. IEEE Access emerged as the most productive journal (90 publications), while IEEE transactions on medical imaging was the most cited source. China led in publication output and international collaboration, followed by the United States. Keyword analysis revealed dominant themes such as deep learning, image segmentation, classification and medical imaging. This bibliometric analysis provides a comprehensive overview of the intellectual structure, research hotspots and collaborative patterns in computer vision-based healthcare research. The findings offer valuable insights for researchers, clinicians and policymakers and identify potential directions for future investigations.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.