ArticleAmerican journal of translational research2024
Artificial intelligence in cardiology: a bibliometric study.
Article in American journal of translational research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- The emerging landscape of artificial intelligence in robot-assisted surgery: a bibliometric and visualization analysis.Updates in surgery · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo perform a comprehensive bibliometric analysis of global publications on the applications of artificial intelligence (AI) in cardiology.
methodsDocuments related to AI in cardiology published between 2002 and 2022 were retrieved from Web of Science Core Collection. R package "bibliometrix", VOSviewers and Microsoft Excel were applied to perform the bibliometric analysis.
resultsA total of 4332 articles were included. United States topped the list of countries publishing articles, followed by China and United Kingdom. The Harvard University was the institution that contributed the most to this field, followed by University of California System and University of London. Disease risk prediction, diagnosis, treatment, disease detection, and prognosis assessment were the research hotspots for AI in cardiology.
conclusionsEnhancing cooperation between different countries and institutions is a critical step in leading to breakthroughs in the application of AI in cardiology. It is foreseeable that the application of machine learning and deep learning in various areas of cardiology will be a research priority in the coming years.
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.