Shaulian Shlomo Y, Gala Dhir, Makaryus Amgad N
Yeshiva College, Yeshiva University, New York, NY, USA.
Department of Internal Medicine, Rutgers New Jersey Medical School, Newark, NJ, USA.
Expert Rev Med Devices. 2025 Aug;22(8):869-879. doi: 10.1080/17434440.2025.2517171. Epub 2025 Jun 11.
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 found to be 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.
在过去几十年中，超声心动图取得了众多技术进步，其中最重要的一项是人工智能（AI）的整合。人工智能算法帮助新手操作员获取具有诊断质量的图像，并使复杂分析自动化。
本综述探讨了人工智能在各种超声心动图模式中的整合，包括经胸、经食管、心内和床旁超声。它研究了人工智能如何在常规、重症监护和复杂心脏成像中增强图像采集、简化分析并提高诊断性能。为进行本综述，使用与论文各部分相关的目标关键词在PubMed上进行搜索，主要关注2020年以后发表的同行评审文章。当发现早期研究具有基础性或被频繁引用时也会纳入。研究结果按主题组织，以突出临床相关性和实际应用。
临床应用中仍然存在挑战，包括算法偏差、伦理问题以及临床医生培训和人工智能监督的需求。尽管如此，人工智能通过精准性和可及性彻底改变心血管护理的潜力仍然无与伦比，如果在心脏超声检查中适当应用和实施，其益处可能远远超过障碍。
