Artificial intelligence in echocardiography: Applications and future directions

Traditional ultrasound methods, particularly cardiac echocardiography, depend predominantly on evidence-based decision trees and risk-scoring systems to guide and standardise clinical decision-making. Although these approaches demonstrate some effectiveness, their inherent rigidity and failure to address non-linear interactions lead to inadequate consideration of individual variability, thereby compromising diagnostic accuracy and consistency. Furthermore, these conventional techniques are vulnerable to human bias, which increases the risk of misdiagnosis. In contrast, artificial intelligence (AI)-based analytical strategies substantially overcome these limitations using machine and deep learning methodologies. Furthermore, AI can autonomously annotate, measure, and evaluate cardiac function, significantly minimising human error, while enhancing diagnostic precision and reliability. In addition, AI can quickly process extensive volumes of imaging data and efficiently quantify cardiovascular pathological features, thereby improving the efficacy and accuracy of cardiovascular disease diagnosis. AI is widely applied in various areas of cardiac echocardiography, including image classification, disease screening, and automated measurements. Given the increasing integration of AI into cardiac echocardiography practices, we present a comprehensive review of the advantages of AI strategies and their practical applications, aiming to provide profound insights that may help promote automated intelligent analysis of echocardiography.