Objective: To investigate the clinical feasibility of single-breath-hold T2-weighted (SBH-T2WI) liver MRI using Artificial Intelligence-assisted Compressed Sensing (ACS) technique in liver imaging as compared with conventional respiratory-triggered T2WI (RT-T2WI). Methods: From January 2021 to October 2021, 81 patients suspected of liver lesions were enrolled in this prospective study. The liver MRI was performed, including both RT-T2WI and ACS SBH-T2WI. Two experienced radiologists reviewed all images of each studied sequence, and recorded the lesion location and the largest diameter of the lesions. The image quality was quantitatively and qualitatively analyzed regarding signal-tonoise ratio (SNR), contrast-to-noise ratio (CNR), contrast ratio (CR), motion artifact, lesion conspicuity, liver boundary sharpness, and overall image quality. The lesion detection and image quality were compared between two sequences using the Chi-square test or Wilcoxon signed-rank test.Results: For lesion detection, 64 lesions were identified in 53 enrolled patients as the reference standard. The average size was 12.09 +/- 7.4 mm for the benign lesions and 45.89 +/- 22.01 mm for the malignant lesions. Of 64 liver lesions, ACS SBH-T2WI detected 60 lesions (93.8%), and RT-T2WI detected 58 lesions (90.6%). For image quality analysis, the motion artifact of ACS SBH-T2WI sequence was significantly reduced compared with the conventional RT-T2WI sequence (p < 0.05). The SNR, liver boundary sharpness, and overall image quality showed no statistical differences between the two sequences. While the CNR, CR, and lesion conspicuity of ACS SBH-T2WI were significantly better than RT-T2WI (all p < 0.05). Conclusions: The SBH-T2WI with ACS technique showed promising performance as it provided significantly better image quality and lesion detectability with a considerable decrease in scanning time as compared with the conventional RT-T2WI.