ArticleDigital health
Artificial intelligence in the diagnosis and management of congenital heart disease in children: A 25-year bibliometric analysis.
Article in Digital health. 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
Objective: We aimed to perform what is, to our knowledge, the first bibliometric analysis focusing on artificial intelligence (AI) applications in paediatric congenital heart disease (CHD) over a 25-year period (2000-2025). We examined the advancements in research, emerging trends, and principal research topics in this field. Materials and Methods: Articles on AI and CHD published between 2000 and 2025 were retrieved. The data sourced from the Web of Science Core Collection encompassed 423 qualifying studies that were evaluated using Cite Space and VOSviewer to examine the contributions of various countries, institutions, authors, journals, and keywords. These visualisation tools facilitated the mapping of collaboration networks, co-citation patterns, and keyword trends. Results: The United States is the main research hotspot in national terms, contributing 40% of the publications in this area, Harvard Medical School is the institution with the most research results, with Pan, Silin being the most prolific researcher. Key research areas include the application of AI in prenatal screening for CHD, diagnosis and treatment of paediatric CHD, long-term management of CHD in children, the role of health professionals, and related risks. Conclusion: As far as we know, this is the first bibliometric analysis dedicated to AI in paediatric CHD. It shows continuous growth and interdisciplinary potential. We emphasise the need for improved collaboration between different fields of study, use of AI in medical practice to assess individual risks (especially regarding medication safety), and policy initiatives to address the equity gap between high-income regions and those with the most CHD cases.
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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 Socratic graph.