ReviewEcho research and practice2018
Artificial intelligence and echocardiography.
Review in Echo research and practice, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 97 papers, 5 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
97 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Artificial Intelligence-Based Echocardiographic Assessment of Diastolic Dysfunction: A Systematic Review.Echocardiography (Mount Kisco, N.Y.) · 2026Pooled it
- Automated and reference methods for the calculation of left ventricular outflow tract velocity time integral or ejection fraction by non-cardiologists: a systematic review on the agreement of the two methods.Journal of clinical monitoring and computing · 2025Pooled it
- Application of artificial intelligence in echocardiography from 2009 to 2024: a bibliometric analysis.Frontiers in medicine · 2025Pooled it
- Detecting Left Heart Failure in Echocardiography through Machine Learning: A Systematic Review.Reviews in cardiovascular medicine · 2022Pooled it
- E-Cardiac Care: A Comprehensive Systematic Literature Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Real-time deep learning-based image guiding and automated left ventricular measurements to reduce test-retest variability.Open heart · 2025Trial
- Artificial Intelligence in Cardiovascular Ultrasound: Clinical Applications, Foundation Models, and the Path to Precision Cardiology.Journal of clinical medicine · 2026Review
- DCFFNet: A New Dual-channel Cross-Feature Fusion Net for Evaluating the Degree of Aortic Valve Calcification Based on Echocardiographic Images.Journal of imaging informatics in medicine · 2026Article
- Agreement of AI-assisted semi-automated right ventricular function analysis using 3D transthoracic echocardiography with artificial intelligence.Journal of echocardiography · 2026Article
- Artificial Intelligence-Enhanced Cardiac Point-of-Care Ultrasound: A Prospective Single-Arm Study.Mayo Clinic proceedings. Digital health · 2026Article
- Point-of-care echocardiography training pathways: a global perspective and the need for standardisation.Open heart · 2026Review
- Alignment Between Cardiologists and AI-Driven Diagnostic Systems: Mixed Methods Study.Journal of medical Internet research · 2026Article
- Advancements and Applications of Focused Assessed Transthoracic Echocardiography: A Narrative Review.Health science reports · 2026Article
- A Deep Learning Approach for Mandibular Condyle Segmentation on Ultrasonography.Journal of imaging informatics in medicine · 2026Article
- Artificial intelligence in cardiovascular medicine: prevention, diagnosis, and intervention.Frontiers in artificial intelligence · 2026Review
- Proposing the ValvUS approach: integrating bedside tests and ultrasonography for severe valvular heart disease diagnosis.Einstein (Sao Paulo, Brazil) · 2026Review
- The Narrative Review: Advancements in Heart Failure Diagnosis and Management using Artificial Intelligence: A New Era of Patient Care.Current cardiology reviews · 2026Review
- Cardiovascular risk assessment enhanced by automated machine learning in a multi-phase study.Scientific reports · 2025Article
- EchoFM: Foundation Model for Generalizable Echocardiogram Analysis.IEEE transactions on medical imaging · 2025Article
- The hope and the hype of artificial intelligence for syncope management.European heart journal. Digital health · 2025Article
37 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Echocardiography plays a crucial role in the diagnosis and management of cardiovascular disease. However, interpretation remains largely reliant on the subjective expertise of the operator. As a result inter-operator variability and experience can lead to incorrect diagnoses. Artificial intelligence (AI) technologies provide new possibilities for echocardiography to generate accurate, consistent and automated interpretation of echocardiograms, thus potentially reducing the risk of human error. In this review, we discuss a subfield of AI relevant to image interpretation, called machine learning, and its potential to enhance the diagnostic performance of echocardiography. We discuss recent applications of these methods and future directions for AI-assisted interpretation of echocardiograms. The research suggests it is feasible to apply machine learning models to provide rapid, highly accurate and consistent assessment of echocardiograms, comparable to clinicians. These algorithms are capable of accurately quantifying a wide range of features, such as the severity of valvular heart disease or the ischaemic burden in patients with coronary artery disease. However, the applications and their use are still in their infancy within the field of echocardiography. Research to refine methods and validate their use for automation, quantification and diagnosis are in progress. Widespread adoption of robust AI tools in clinical echocardiography practice should follow and have the potential to deliver significant benefits for patient outcome.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.