Objective To evaluate the accuracy of an artificial intelligence (AI) model for automatic measurement of echocardiographic Doppler parameters and to quantify its effect of reducing sonographers’ operation time and hand related operational load. Methods Doppler spectrum images such as pulmonary valve, aortic valve, mitral forward spectrum, septal and lateral tissue Doppler spectrum of 1668 patients were retrospectively collected. The view classification and measurements agreed upon by two senior sonographers were used as the gold standard. The dataset was split into training and validation sets at a ratio of 4 ∶ 1. A deep learning model was constructed based on ResNet50 (view classification), YOLOv5 (cardiac cycle detection) and HRNet (key point detection). After model deployment, 747 Doppler images were prospectively collected for accuracy validation. Additionally, 661 patients were enrolled and divided into an AI-assisted group (n = 330) and a non AI assisted group (n = 331). Examination time, active operation time, stationary time and trackball operation counts were recorded using an attitude-measurement sensor. The differences were compared between the two groups. Results The overall view classification accuracy of the AI model ranged from 90. 5% to 99. 3%. The intraclass correlation coefficients (ICC) between AI measurements and the gold standard for each Doppler parameter ranged from 0. 9902 to 0. 9967, with 95% confidence intervals between 0. 99 and 1. 00. Bland Altman analysis showed that the bias was close to zero. The median examination time and median active operation time in the AI-assisted group were significantly shorter than those in the non-AI-assisted group (both P<0. 001). There was no statistically significant difference in stationary time between the two groups ( P = 0. 068). The median number of trackball operations in the AI-assisted group was significantly lower than that in the non-AI-assisted group (P< 0. 001). Conclusions The proposed AI model can automatically measure echocardiographic Doppler parameters with high accuracy. In real world clinical practice, it significantly shortens the examination time and reduces the hand related operational load. This provides the quantifiable evidence for lowering the risk of repetitive strain injuries and improves the work efficiency for sonographers. It has good clinical translational value.