{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["15(1)"],"submitter":["Sakamoto A"],"pubmed_abstract":["<h4>Background</h4>This trial evaluated whether an artificial intelligence (AI)-based automatic analysis for echocardiography could improve sonographer workflow in real-world clinical practice.<h4>Methods</h4>In 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.<h4>Resu"],"journal":["Journal of the American Heart Association"],"pagination":["e045637"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12909003"],"repository":["biostudies-literature"],"pubmed_title":["Artificial Intelligence-Based Automated Echocardiographic Analysis and the Workflow of Sonographers: A Randomized Crossover Trial (AI-Echo RCT)."],"pmcid":["PMC12909003"],"pubmed_authors":["Sato E","Minamino T","Sugihara K","Nakamura Y","Miyazaki S","Sakamoto A","Murata A","Ashikawa Y","Kaneko T","Kagiyama N"],"additional_accession":[]},"is_claimable":false,"name":"Artificial Intelligence-Based Automated Echocardiographic Analysis and the Workflow of Sonographers: A Randomized Crossover Trial (AI-Echo RCT).","description":"<h4>Background</h4>This trial evaluated whether an artificial intelligence (AI)-based automatic analysis for echocardiography could improve sonographer workflow in real-world clinical practice.<h4>Methods</h4>In 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.<h4>Resu","dates":{"release":"2026-01-01T00:00:00Z","publication":"2026 Jan","modification":"2026-07-16T00:08:32.687Z","creation":"2026-07-09T10:25:56.736Z"},"accession":"S-EPMC12909003","cross_references":{"pubmed":["41404733"],"doi":["10.1161/JAHA.125.045637"]}}