Tolu-Akinnawo Oluwaremilekun Zeth, Ezekwueme Francis, Omolayo Olukunle, Batheja Sasha, Awoyemi Toluwalase
Department of Internal Medicine, Meharry Medical College, Nashville, Tennessee, USA.
Department of Internal Medicine, University of Pittsburgh Medical Center, McKeesport, Pennsylvania, USA.
Clin Cardiol. 2025 Jan;48(1):e70087. doi: 10.1002/clc.70087.
Technological advancements in artificial intelligence (AI) are redefining cardiac imaging by providing advanced tools for analyzing complex health data. AI is increasingly applied across various imaging modalities, including echocardiography, magnetic resonance imaging (MRI), computed tomography (CT), and nuclear imaging, to enhance diagnostic workflows and improve patient outcomes.
Integrating AI into cardiac imaging enhances image quality, accelerates processing times, and improves diagnostic accuracy, enabling timely and personalized interventions that lead to better health outcomes.
A comprehensive literature review was conducted to examine the impact of machine learning and deep learning algorithms on diagnostic accuracy, the detection of subtle patterns and anomalies, and key challenges such as data quality, patient safety, and regulatory barriers.
Findings indicate that AI integration in cardiac imaging enhances image quality, reduces processing times, and improves diagnostic precision, contributing to better clinical decision-making. Emerging machine learning techniques demonstrate the ability to identify subtle cardiac abnormalities that traditional methods may overlook. However, significant challenges persist, including data standardization, regulatory compliance, and patient safety concerns.
AI holds transformative potential in cardiac imaging, significantly advancing diagnosis and patient outcomes. Overcoming barriers to implementation will require ongoing collaboration among clinicians, researchers, and regulatory bodies. Further research is essential to ensure the safe, ethical, and effective integration of AI in cardiology, supporting its broader application to improve cardiovascular health.
人工智能（AI）的技术进步正在通过提供分析复杂健康数据的先进工具来重新定义心脏成像。AI越来越多地应用于各种成像模式，包括超声心动图、磁共振成像（MRI）、计算机断层扫描（CT）和核成像，以优化诊断流程并改善患者预后。
将AI整合到心脏成像中可提高图像质量、加快处理速度并提高诊断准确性，从而实现及时且个性化的干预，带来更好的健康结果。
进行了一项全面的文献综述，以研究机器学习和深度学习算法对诊断准确性、细微模式和异常检测的影响，以及数据质量、患者安全和监管障碍等关键挑战。
研究结果表明，在心脏成像中整合AI可提高图像质量、缩短处理时间并提高诊断精度，有助于做出更好的临床决策。新兴的机器学习技术显示出识别传统方法可能忽略的细微心脏异常的能力。然而，重大挑战依然存在，包括数据标准化、法规遵从性和患者安全问题。
AI在心脏成像中具有变革潜力，可显著推进诊断和改善患者预后。克服实施障碍需要临床医生、研究人员和监管机构之间持续合作。进一步的研究对于确保AI在心脏病学中的安全、符合伦理和有效整合至关重要，有助于其更广泛地应用以改善心血管健康。
