Artificial intelligence in the diagnosis and management of congenital heart disease in children: A 25-year bibliometric analysis

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.