Stamate Elena, Piraianu Alin-Ionut, Ciobotaru Oana Roxana, Crassas Rodica, Duca Oana, Fulga Ana, Grigore Ionica, Vintila Vlad, Fulga Iuliu, Ciobotaru Octavian Catalin
Department of Cardiology, Emergency University Hospital of Bucharest, 050098 Bucharest, Romania.
Faculty of Medicine and Pharmacy, University "Dunarea de Jos" of Galati, 35 AI Cuza Street, 800010 Galati, Romania.
Diagnostics (Basel). 2024 May 26;14(11):1103. doi: 10.3390/diagnostics14111103.
Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology.
We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology.
The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology.
人工智能（AI）能够从根本上改变人类体验的几乎每个方面。在医学领域，人工智能有众多应用，并且在相对较短的时间内取得了重大进展。心脏病学也未能免受这一趋势的影响，算法在数据分析、模式发现、异常识别和治疗决策中发挥重要作用的出版物数量呈指数级增长，这一事实证明了这一点。此外，随着技术发展，出现了新的机器学习（ML）和深度学习（DL）模型，它们能够探索人工智能在心脏病学中的各种应用，包括预防、心血管成像、电生理学、介入心脏病学等领域。从这个意义上说，本文旨在对心脏病学中人工智能应用的当前状态提供一个总体概述。
我们识别并纳入了与当前研究直接相关的200篇论文的子集，涵盖了广泛的应用。因此，本文以总结的方式介绍了人工智能在心血管成像、算术学、临床或急诊心脏病学、心血管预防和介入手术中的应用。来自高科学文献的最新研究证明了在心脏病学不同分支中使用人工智能的可行性和优势。
人工智能在心脏病学中的整合为通过降低错误率提高准确性和提高心血管实践效率提供了有前景的前景。从预测猝死风险或对心脏再同步治疗的反应能力到肺栓塞的诊断或瓣膜疾病的早期检测，人工智能算法已显示出其减轻人为错误并提供可行解决方案的潜力。同时，强调了所研究小样本带来的局限性以及道德实施带来的挑战；这些涉及责任和决策过程的法律影响，确保患者保密和数据安全。所有这些构成了未来的研究方向，将使人工智能融入心脏病学的发展进程。
