Background: Programmed death-ligand 1 (PD-L1) expression is a predictive marker for immune checkpoint inhibitors (ICI) treatment in various cancer types. The evaluation of PD-L1 expression level by combined positive score (CPS) correlates with immunotherapeutic response in biliary tract, colorectum, liver, pancreas, prostate, and gastric cancers. This study aimed to assess the performance of an artificial intelligence (AI)-powered PD-L1 CPS analyzer on these six cancer types, and to investigate whether the AI assistance could improve concordance among pathologists. Methods: Lunit SCOPE PD-L1 CPS was developed with 1.51 x 10⁶ tumor cells and 8.73 x 10⁵ immune cells from 2,372 PD-L1 stained whole-slide images (WSI) or tissue microarray cores from various cancer and normal tissues. The algorithm consisted of tissue area segmentation and cell detection AI models. The AI models calculated the CPS by detecting tumor cells over the tumor area and immune cells over the tumor and adjacent area. The model performance was validated on 135 PD-L1 stained WSIs including the six cancer types, which were interpreted by three pathologists. The concordant CPS classification (≥1 or <1) by two or more pathologists was considered as the consensus. Each pathologist revisited to evaluate WSIs with AI assistance (including visualization and scoring) if there was a discrepancy between the pathologist and the AI model. Results: Of 135 WSIs, 122 (90.4%) were classified as the same CPS subgroup by all three pathologists. The CPS ≥1 and <1 subgroup included 67 (49.6%) and 68 (50.4%) cases, respectively. The overall percent agreement (OPA) of the AI model to the consensus of pathologists was 84.4%, ranging from 78.3% (liver) to 91.3% (biliary tract). The AI-assisted re-evaluation by three pathologists was performed in 17, 19, and 22 WSIs, respectively. According to the AI-assisted revision, the unanimous agreement level was increased to 92.6% (125 cases). The OPA of the AI model to the consensus of pathologists was also increased to 91.9%, ranging from 82.6% (liver) to 100.0% (pancreas). Conclusions: This study shows that an AI-powered PD-L1 CPS analyzer can evaluate the CPS in the six cancer types analyzed here at a comparable level to pathologists. AI assistance can improve the concordance of pathologists' CPS interpretation.