Aug 2026· Journal of Cardiovascular Imaging· Vol 34· 0 citations· 38 references
Medicine
TL;DR
This review evaluates current evidence, identifies existing gaps, and outlines the requirements for responsible clinical implementation of AI in echocardiography.
Abstract
Echocardiography remains the cornerstone of cardiovascular imaging. However, traditional workflows including manual acquisition, sequential measurement, and expert interpretation face challenges from increased clinical demand, workforce shortage, and the physical burden of repetitive scanning. Artificial intelligence (AI) has begun to address these issues, transitioning from proof-of-concept to prospective clinical evaluations. Recent evidence suggests that AI integration reduces examination time and automates measurements, enabling more comprehensive data collection while mitigating sonographer fatigue and improving image quality. The sonographer's role is accordingly evolving from conventional measurement to active verification. AI applications in echocardiography now extend beyond ejection fraction to integrated assessments of myocardial texture and Doppler hemodynamics. New model architectures incorporate both structural and functional evaluations, reflecting clinical reasoning of the expert. These methods are being applied to valvular heart disease, cardiomyopathy, and pericardial disorders. Clinical implementation of AI in echocardiography requires more than high accuracy. Current evidence is limited by reliance on single-center studies, inconsistent performance across platforms, and the potential for automation bias in high-volume settings. This review evaluates current evidence, identifies existing gaps, and outlines the requirements for responsible clinical implementation of AI.
Artificial intelligence (AI) is increasingly used in echocardiography and point-of-care ultrasound (POCUS) to support image acquisition, view recognition, image-quality assessment, segmentation, automated quantification, disease classification, reporting, and bedside decision support. This narrative review summarizes clinically relevant applications, with emphasis on clinical integration, pediatric and congenital heart disease considerations, and safe implementation. The strongest clinical evidence supports automated left ventricular segmentation and ejection fraction estimation, AI-guided acquisition, and workflow efficiency. Video-based deep learning has enabled beat-to-beat assessment of ventricular function, and a randomized workflow trial showed that AI-generated initial ejection fraction assessment was noninferior to sonographer assessment and required fewer cardiologist corrections. Regulatory-authorized acquisition and analysis tools demonstrate growing clinical adoption for specified adult indications. Recent multiview and disease-phenotyping models extend AI toward more comprehensive interpretation, while AI-enabled POCUS may improve focused image acquisition by non-expert users. However, external validation, pediatric and congenital heart disease data, cross-device generalizability, clinical outcome evidence, uncertainty communication, automation bias, and medicolegal responsibility remain important limitations. AI should currently be viewed as an augmentative rather than autonomous technology. The safest near-term model is human-AI collaboration, in which validated tools improve acquisition, reproducibility, and workflow while clinicians retain responsibility for interpretation and patient-centered decisions. Pediatric and congenital heart disease applications require age- and anatomy-specific datasets, multicenter validation, local performance monitoring, and clinician-supervised deployment. AI can assist across the echocardiography workflow, from acquisition guidance and view recognition to segmentation, quantification, disease screening, and structured reporting. The most mature clinical evidence supports left ventricular segmentation, ejection fraction estimation, AI-guided acquisition, and workflow efficiency rather than autonomous diagnosis. AI-enabled POCUS may improve acquisition by non-expert users, but image adequacy, interpretation, and clinical integration must remain clinician-supervised. Pediatric and congenital heart disease applications are promising but remain less mature than adult applications and require anatomy-specific datasets and multicenter validation. Safe implementation requires external validation, local monitoring, bias assessment, uncertainty display, audit trails, and institutional governance.
F. Savorgnan, Pranathi Pilla, Sarah Visokay et al.· Current Pediatrics Reports· 0 citations
Echocardiography is a foundational imaging modality for assessing cardiac structure and function, with a long history of technological advancement. As artificial intelligence (AI) becomes increasingly embedded across healthcare, its potential to enhance the echocardiography workflow, from image acquisition and analysis to reporting and risk stratification, has expanded rapidly. Research activity in this field has grown substantially, yet clinical adoption remains variable and often limited by practical, technical, and governance challenges. In response, the British Society of Echocardiography has developed this position statement to provide a structured, consensus‑driven evaluation of the opportunities, limitations, and requirements for the safe, equitable, and effective integration of AI into echocardiography services. Although centred on the UK context, the principles outlined may be relevant to other healthcare systems with similar models of echocardiography delivery.
S. Bennett, C. Wild, Maria F Paton et al.· Echo Research and Practice· 0 citations
Heart failure (HF) is increasingly understood not as a single, uniformly treated diagnosis but as a heterogeneous syndrome requiring aetiological clarification, in which cardiac imaging is central. As the opening article of this journal's ‘Imaging in Heart Failure’ section, this review surveys the technologies currently reshaping HF imaging and sets out the section's scope and priorities, framing the shift from a descriptive, modality-siloed practice toward an integrated, predictive, patient-specific discipline. Artificial intelligence (AI) now delivers expert-level echocardiography automation, guides image acquisition by novices in resource-limited settings, detects aetiologies such as transthyretin amyloid cardiomyopathy from a single acquisition and enables deep phenotyping through radiomics and vendor-agnostic strain analysis. Handheld, AI-enabled point-of-care ultrasound extends imaging-guided triage beyond the echocardiography laboratory. Cardiovascular magnetic resonance (CMR) advances — parametric mapping, four-dimensional flow, diffusion tensor imaging, spectroscopy, and accelerated reconstruction — broaden tissue and metabolic characterisation, including patients with implanted devices. Molecular imaging with novel positron emission tomography tracers and hyperpolarised magnetic resonance is moving from depicting the structural consequences of disease to imaging active pathobiology, while photon-counting computed tomography and image-derived digital twins support one-stop structural assessment and in-silico prediction of therapy response. The convergence of AI, molecular imaging and advanced precision is transforming HF imaging from better pictures into smarter, integrated, personalised data that directly inform care. Realising this promise will require rigorous validation, attention to algorithmic bias and generalisability, demonstrated cost-effectiveness, curricular reform, and equitable access. This section aims to critically appraise these innovations and their translation into practice.
M. Hundertmark· Current Heart Failure Report...· 0 citations
Overall, AI-empowered echocardiography holds substantial promise for advancing precision diagnosis, risk stratification, and personalized management of HCM, facilitating a transition toward more intelligent and individualized cardiovascular care.
Miao Zhang, Shan-Shan Yuan, Hong-Yan Dai et al.· Frontiers in Cardiovascular...· 0 citations
A multiview video-language framework improved report retrieval compared with conventional image-based approaches and support the utility of video-based, multiview representation learning for echocardiographic report retrieval.
R. Takizawa, Chiemi Yamazaki, S. Kodera et al.· JACC: Asia· 0 citations
This review summarizes recent advances in the use of AI to facilitate diastolic function assessment and addresses limitations of AI including explainability, generalizability, regulatory considerations, and integration into clinical workflows.
Teresa S. M. Tsang, Darwin Yeung· Current Opinion in Cardiolog...· 0 citations
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