Abstract: Objective: To investigate the performance of an artificial-intelligence–based metal artifact correction algorithm (AIMAC) on pelvic computed tomography (CT) after total hip arthroplasty. Methods: This retrospective study included data from 34 patients with a history of hip arthroplasty who underwent pelvic CT at our institution between September 2022 and December 2024. Raw data were reconstructed using three methods: routine reconstruction (Karl 3D, level 5), conventional metal artifact correction (MAC), and the AIMAC For subjective evaluation, two radiologists independently scored overall image quality using a five-point scale in a blinded manner, and inter-reader agreement was assessed using the kappa statistic. For objective evaluation, regions of interest were drawn to calculate CT-number differences between the artifact-affected and reference slices (ΔCT_muscle and ΔCT_fat), image noise (standard deviation SD_artifact and SD_reference), the metal artifact index (MAI), and the contrast-to-noise ratio (CNR). Overall comparisons among the three reconstructions were performed using repeated-measures analysis of variance or the Friedman test, according to normality. Pairwise comparisons were conducted using the paired t-test or the Wilcoxon signed-rank test, with Holm–Bonferroni adjustment for multiple comparisons (P*). Results: Inter-reader agreement for overall image quality was good (kappa: 0.866, 0.816, and 0.794). Overall subjective scores differed significantly among the three methods, with the AIMAC scoring higher than MAC, and MAC scored higher than routine reconstruction (all pairwise P* < 0.0001). For objective metrics, the ΔCT_muscle, ΔCT_fat, SD_artifact, MAI, and CNR all had significant differences among the three methods, with the AIMAC outperforming MAC and routine reconstruction. For SD_reference, MAC was lower than routine reconstruction and AIMAC, whereas no significant difference was observed between routine reconstruction and AIMAC. Conclusion: For pelvic CT after hip arthroplasty, the AIMAC more effectively reduced metal artifacts, improved CT-number stability, and enhanced overall image quality compared with routine reconstruction and conventional MAC.