ReviewNPJ cardiovascular health2025
Contemporary applications of artificial intelligence and machine learning in echocardiography.
Review in NPJ cardiovascular health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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
10 citing papers in PubMed.
- Systolic Anterior Motion After Mitral Valve Repair: Echocardiographic Prediction, Surgical Prevention and Perioperative Management.Journal of clinical medicine · 2026Review
- Diagnostic Tests for Stage B Heart Failure.Current cardiology reports · 2026Review
- Cardiac imaging in Chagas cardiomyopathy for phenotypic characterisation and risk stratification.Heart failure reviews · 2026Review
- The role of artificial intelligence in early detection and risk prediction of ischemic heart disease.Annals of medicine and surgery (2012) · 2026Review
- Artificial intelligence-empowered echocardiography: an updated review of clinical management in hypertrophic cardiomyopathy.Frontiers in cardiovascular medicine · 2026Review
- Beyond Standard Parameters: Precision Hemodynamic Monitoring in Patients on Veno-Arterial ECMO.Journal of personalized medicine · 2025Review
- AI-Assisted Handheld Echocardiography by Nonexpert Operators: A Narrative Review of Prospective Studies.Cureus · 2025Review
- Point-of-Care Transesophageal Echocardiography in Emergency and Intensive Care: An Evolving Imaging Modality.Biomedicines · 2025Review
- Development and Validation of Echocardiography Artificial Intelligence Models: A Narrative Review.Journal of clinical medicine · 2025Review
- Right Heart Failure in Critical and Chronic Care: Current Concepts, Challenges and Mechanical Support Strategies.Medical sciences (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) and machine learning (ML) are reshaping echocardiography by automating image analysis, reducing variability, and enhancing diagnostic accuracy through tasks such as view classification, image segmentation, and outcome prediction. Key applications include left ventricular ejection fraction assessment and improved valvular disease diagnostics. Limitations include challenges with generalizability, interpretability, and integration into diverse clinical settings. This article provides a contemporary review of AI and ML applications in echocardiography.
Identifiers
What OpenQuestion holds
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.