Artificial Intelligence–Based Automated Echocardiographic Analysis and the Workflow of Sonographers: A Randomized Crossover Trial (AI‐Echo RCT)

Abstract

BACKGROUND: This trial evaluated whether an artificial intelligence (AI)-based automatic analysis for echocardiography could improve sonographer workflow in real-world clinical practice. METHODS: 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. RESULTS: <0.001). CONCLUSIONS: This 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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