aceArtificial Intelligence in Echocardiography: A Critical Review of Models, Validation, and Clinical Impact
Objective To critically review artificial intelligence (AI) in echocardiography, linking model design and technical performance to validation and clinical use. Methods We synthesized literature assembled through Web of Science and Scopus collections and targeted revision searches. Evidence was organized by echocardiographic task, model paradigm, data source, evaluation protocol, and level of clinical validation. Results AI applications span acquisition guidance, image analysis, quantitative measurement, disease…
For view recognition, segmentation, and selected measurements, AI has shown reproducible performance under aligned protocols, and prospective trials confirm workflow gains in echocardiography.
Evidence that echocardiography AI improves clinical decisions or patient outcomes remains limited, and generalizability is constrained by uneven datasets and inconsistent evaluation protocols.
The review notes that most studies are retrospective, few test cross-site transportability or clinical impact, and evidence for improved decisions or patient outcomes is limited.
Evidence
- Peer-reviewedIEEE Transactions on Biomedical Engineering2026-10-14
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Truvace Impact Record TRV-2026-1924, v1: “Artificial Intelligence in Echocardiography: A Critical Review of Models, Validation, and Clinical Impact.” Truvace, 2026-10-19. /record/TRV-2026-1924 (accessed at citation time). sha256 b3da653f4d0631be…
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