ReviewPediatric cardiology2025
A Paradigm Shift in Congenital Heart Disease: A Scientometric Portrait of the Rise of Computational Intelligence.
Review in Pediatric cardiology, 2025. 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
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The application of artificial intelligence (AI) to congenital heart disease (CHD) has become a rapidly expanding field, promising to transform diagnostics, prognostics, and management. However, a comprehensive map of the global research landscape, its key contributors, thematic structure, and evolutionary trends is currently lacking. This study provides a bibliometric analysis to delineate this dynamic domain. We conducted a systematic search of the Web of Science Core Collection for all publications related to AI in CHD up to December 31, 2024. Bibliometric analyses were performed using VOSviewer and the Bibliometrix R package to map publication trends, international collaboration networks, leading contributors, and the conceptual structure of the field through co-occurrence and thematic mapping. Our analysis of 500 publications revealed an exponential growth in research output since 2020. The United States, China, and the United Kingdom were the most productive countries, forming central hubs in a robust international collaboration network. Harvard University and the University of London were the leading institutions. Thematic analysis identified six dominant research clusters: (1) AI-enhanced surgical/interventional support, (2) prenatal diagnosis via imaging, (3) disease-specific outcome modeling (e.g., Tetralogy of Fallot), (4) advanced imaging analytics with deep learning, (5) comprehensive risk and outcome management, and (6) pediatric cardiology applications. Temporal analysis confirmed a decisive recent shift towards deep learning methodologies, and analysis of highly cited works underscored the impact of AI in imaging and prognostic modeling. The convergence of AI and CHD has matured into a vibrant, clinically-focused research field with a clear technological trajectory towards deep learning. This global research panorama highlights established strengths in imaging and diagnostics while pointing to emerging frontiers in personalized medicine and interventional support. Fostering greater collaboration and focusing on clinical translation, model explainability, and equity will be crucial for realizing the full potential of this AI-driven transformation in improving care for patients with CHD.
Indexed as
Identifiers
41269260What Socratic 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 Socratic graph.