Trial reportJournal of the American Heart Association2026
Artificial Intelligence-Based Automated Echocardiographic Analysis and the Workflow of Sonographers: A Randomized Crossover Trial (AI-Echo RCT).
Trial report in Journal of the American Heart Association, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
What it found
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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
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- PRIME 2.0: Proposed Requirements for Cardiovascular Imaging-Related Multimodal-AI Evaluation: An Updated Checklist.JACC. Cardiovascular imaging · 2026Guideline
- Automated echocardiographic measurements for longitudinal monitoring of ATTR cardiomyopathy: agreement and repeatability analysis.The international journal of cardiovascular imaging · 2026Article
- Reinventing the echocardiography workflow: from manual quantification to artificial intelligence-driven comprehensive interpretation.Journal of cardiovascular imaging · 2026Review
- Evaluating sex bias in artificial intelligence-based echocardiographic analysis: a retrospective study on individuals with normal cardiac function and patients with systolic and diastolic dysfunction.Cardiovascular ultrasound · 2026Article
- Artificial intelligence in cardiovascular care: a systematic review and meta-analysis of randomised controlled trials.EClinicalMedicine · 2026Article
- Artificial Intelligence in Cardiovascular Medicine: A Giant Step in Personalized Medicine?Journal of personalized medicine · 2026Review
- Automated Phenotyping of Mitral Stenosis Using Deep Learning.medRxiv : the preprint server for health sciences · 2026Article
- Artificial intelligence-enhanced echocardiography in cardiovascular disease management.Nature reviews. Cardiology · 2026Review
- The Emerging Role of Artificial Intelligence in the Assessment of Valvular Heart Disease with Cardiac Imaging.Current cardiology reports · 2026Review
- AI-assisted longitudinal cardiac phenotyping identifies domain-specific remodeling patterns in Fabry cardiomyopathy.Frontiers in artificial intelligence · 2026Article
- Comprehensive aortic stenosis characterization using multi-view deep learning.medRxiv : the preprint server for health sciences · 2025Article
- Artificial Intelligence and Echocardiography: From Literacy to Leadership, Redefining Roles, Responsibilities, and Sustainability in the Artificial Intelligence-driven Echocardiography Era.Journal of cardiovascular echographyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis trial evaluated whether an artificial intelligence (AI)-based automatic analysis for echocardiography could improve sonographer workflow in real-world clinical practice.
methodsIn a single-center crossover trial, 4 sonographers were randomly assigned to use AI assistance (AI days) or manual workflow (non-AI days) on a daily basis. The AI tool automatically measured echocardiographic parameters, allowing sonographers to focus on verifying AI-generated values. Expert echocardiologists finalized all reports. The primary end point was examination efficiency, measured by examination time per patient and number of examinations per day. Secondary end points included sonographer fatigue, the number of analyzed echocardiographic parameters, and image quality.
resultsA total of 585 patients were scanned over 38 study days (AI days, 317; non-AI days, 268) between January 30 and March 26, 2024. Baseline characteristics were comparable between groups. AI assistance significantly reduced examination time (13.0±3.5 minutes versus 14.3±4.2 minutes,
conclusionsThis real-world randomized trial demonstrated that AI-based echocardiographic analysis enhances workflow efficiency, reduces sonographer fatigue, and improves image quality without compromising diagnostic integrity. AI integration holds promise for optimizing high-volume echocardiography workflows. REGISTRATION: URL: https://center6.umin.ac.jp. Unique identifier: UMIN000053259.
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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.