Integration of artificial intelligence into cardiac ultrasonography practice.
Authors
Affiliations (4)
Affiliations (4)
- Yeshiva College, Yeshiva University, New York, New York, [email protected].
- Department of Internal Medicine, Rutgers New Jersey Medical School, Newark, USA.
- Department of Cardiology, Cardiovascular Institute, Northwell Health, New Hyde Park, NY.
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell.
Abstract
Over the last several decades, echocardiography has made numerous technological advancements, with one of the most significant being the integration of artificial intelligence (AI). AI algorithms assist novice operators to acquire diagnostic-quality images and automate complex analyses. This review explores the integration of AI into various echocardiographic modalities, including transthoracic, transesophageal, intracardiac, and point-of-care ultrasound. It examines how AI enhances image acquisition, streamlines analysis, and improves diagnostic performance across routine, critical care, and complex cardiac imaging. To conduct this review, PubMed was searched using targeted keywords aligned with each section of the paper, focusing primarily on peer-reviewed articles published from 2020 onward. Earlier studies were included when foundational or frequently cited. The findings were organized thematically to highlight clinical relevance and practical applications. Challenges persist in clinical application, including algorithmic bias, ethical concerns, and the need for clinician training and AI oversight. Despite these, AI's potential to revolutionize cardiovascular care through precision and accessibility remains unparalleled, with benefits likely to far outweigh obstacles if appropriately applied and implemented in cardiac ultrasonography.