The International Journal of Medical Discoveries (IJMD) is a peer-reviewed, open-access journal dedicated to advancing the understanding, innovation, and application of medical science. Our mission is to serve as a platform for the dissemination of cutting-edge research and discoveries that shape the future of healthcare and important medical discoveries worldwide.
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Artificial intelligence (AI)-assisted measurement is moving from experimental image analysis toward routine echocardiographic workflow support. Existing studies show that automated systems can perform selected measurements accurately, improve reproducibility, and reduce measurement and reporting time. This article examines those dimensions through a mixed-methods analytical dataset comprising 120 quantitative records and a qualitative subsample of 20 records. Within the quantitative dataset, 74 of 120 records (61.7%) represented frequent AI use, defined as daily or several-timesper-week use. AI-assisted chamber measurement was represented in 91 records (75.8%), ventricular function estimation in 84 (70.0%), Doppler measurement in 67 (55.8%), image/view assistance in 61 (50.8%), and strain analysis in 52 (43.3%). Verification was common, with 101 records (84.2%) categorized as usually verifying automated outputs. High trust was present in 58 records (48.3%), moderate trust in 45 (37.5%), and low trust in 17 (14.2%). Positive perceptions were common for workflow efficiency, Author 2 measurement consistency, measurement-time reduction, and professional productivity. Spearman associations were .608 between perceived accuracy and trust, .460 between frequent use and workflow benefit, .404 between frequent use and trust, and .431 between formal training and trust. In the multivariable model, formal training, frequent use, high workflow benefit, and high perceived accuracy were positively associated with high AI acceptance. The qualitative strand was organized around five themes: Automation Redistributes Expertise; Verification Becomes a New Competency; Trust Is Conditional, Not Binary; Identity Shifts from Producer to Supervisor, and Organizational Readiness Shapes Experience. Integration through a joint display indicates convergence around an augmentation model in which automation reduces repetitive measurement work while professional judgment remains responsible for verification, interpretation, and quality control.
Echocardiography; artificial intelligence; automated measurement; workflow integration; trust; verification; professional identity; training; mixed methods.