Artificial intelligence in cardiovascular procedures: a bibliometric and visual analysis study

Authors

Koushik Rao Gadhachanda, Boston University Biology, Boston, Massachusetts, USA.
Mohammed Dheyaa Marsool Marsool, Department of Internal Medicine, Al-Kindy College of Medicine, University of Baghdad, Baghdad, Iraq.
Ali Bozorgi, Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran.
Daniyal Ameen, Department of Internal Medicine, Yale New Haven Health Bridgeport Hospital, Bridgeport, Connecticut, USA.
Sandeep Samethadka Nayak, Department of Internal Medicine, Yale New Haven Health Bridgeport Hospital, Bridgeport, Connecticut, USA.
Amir Nasrollahizadeh, Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran.
Abdulhadi Alotaibi, Department of Medicine, Vision Colleges, Riyadh, Saudi Arabia.
Alireza Farzaei, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Mohammad-Hossein Keivanlou, Guilan University of Medical Sciences, Rasht, Iran.
Soheil Hassanipour, Guilan University of Medical Sciences, Rasht, Iran.
Ehsan Amini-Salehi, Guilan University of Medical Sciences, Rasht, Iran.
Anil Kumar Jonnalagadda, Department of Cardiology, John Peter Smith Hospital, Fort Worth, Texas.

Document Type

Article

Publication Title

Annals of medicine and surgery (2012)

Abstract

BACKGROUND: The integration of artificial intelligence (AI) into cardiovascular procedures has significantly advanced diagnostic accuracy, outcome prediction, and robotic-assisted surgeries. However, a comprehensive bibliometric analysis of AI's impact in this field is lacking. This study examines research trends, key contributors, and emerging themes in AI-driven cardiovascular interventions. METHODS: We retrieved relevant publications from the Web of Science Core Collection and analyzed them using VOSviewer, CiteSpace, and Biblioshiny to map research trends and collaborations. RESULTS: AI-related cardiovascular research has grown substantially from 1993 to 2024, with a sharp increase from 2020 to 2023, peaking at 93 publications in 2023. The USA (127 papers), China (79), and England (31) were the top contributors, with Harvard University leading institutional output (17 papers). Frontiers in Cardiovascular Medicine was the most prolific journal. Core research themes included "machine learning," "mortality," and "cardiac surgery," with emerging trends in "association," "implantation," and "aortic stenosis," underscoring AI's expanding role in predictive modeling and surgical outcomes. CONCLUSION: AI demonstrates transformative potential in cardiovascular procedures, particularly in diagnostic imaging, predictive modeling, and patient management. This bibliometric analysis highlights the growing interest in AI applications and provides a framework for integrating AI into clinical workflows to enhance diagnostic accuracy, treatment strategies, and patient outcomes.

First Page

2187

Last Page

2203

DOI

10.1097/MS9.0000000000003112

Publication Date

4-1-2025

Recommended Citation

Gadhachanda, Koushik Rao; Marsool Marsool, Mohammed Dheyaa; Bozorgi, Ali; Ameen, Daniyal; Nayak, Sandeep Samethadka; Nasrollahizadeh, Amir; Alotaibi, Abdulhadi; Farzaei, Alireza; Keivanlou, Mohammad-Hossein; Hassanipour, Soheil; Amini-Salehi, Ehsan; and Jonnalagadda, Anil Kumar, "Artificial intelligence in cardiovascular procedures: a bibliometric and visual analysis study" (2025). Internal Medicine. 238.
https://scholar.bridgeporthospital.org/internal_medicine/238

Identifier

40212154 (pubmed); PMC11981337 (pmc); 10.1097/MS9.0000000000003112 (doi); AMSU-D-24-01968 (pii)