Artificial intelligence and cardiology in Latin America: a 5-year bibliometric analysis of scientific research
- Fabián A. Chávez-Ecos,
- Carlos Quispe-Vicuña,
- Leonardo J. Uribe-Cavero,
- Wagner Ríos-García,
- Linda A. Pasapera-Chacaliaza,
- Luis A. Javier-Contreras
- Universidad Científica del Sur,
- Red de Cardiología y Salud Pública (RCSP),
- Universidad Nacional San Luis Gonzaga de Ica,
Open access
Publication Information
Output type
Original language
SpanishPages from-to (Number of pages)
Pages 120-129 (10 pages)Journal (Volume, Issue Number)
Archivos de cardiologia de Mexico (Volume 96, Issue 2)Publication milestones
- Published - 01/04/2026
Publication status
ISSN
1405-9940Publication IDs
- Scopus: 105040162983
- PubMed: 41129828
Abstract
Objective: Artificial intelligence (IA) is transforming healthcare by enhancing diagnosis and treatment, with up to 90% accuracy in cardiology. However, its adoption faces challenges, including limited training and resources in some regions. While scientific output in Latin America and Caribe (LAC) has grown, it remains low compared to other regions, underscoring the need for innovative solutions to the cardiovascular health crisis. This study analyzes AI-related cardiology research in LAC from 2018 to 2023. Method: This descriptive scientometric study used the Scopus database to analyze AI in cardiology research in LAC. SciVal, VOSviewer, and R Studio were applied to assess publication volume, collaboration types, citations, and research networks. Results: A total of 152 documents, 1,054 citations, and 1,095 authors were identified, averaging 6.9 citations per document. Key topics included atrial fibrillation, percutaneous coronary intervention, and cardiac monitoring. Colombia, Argentina, and Mexico led in scientific output, with international collaboration accounting for 63.8% of publications and an upward trend in research output over time. Conclusions: AI-related cardiology research in LAC is growing, but certain limitations. This analysis highlights key areas and the need to enhance scientific production. It provides a base for future studies and collaborations to address the cardiovascular health crisis and expand AI adoption in cardiovascular care.
