Background and Aims The expression of human epidermal growth factor receptor 2 (HER-2) in gastric cancer exhibits significant heterogeneity, and comprehensive evaluation of HER-2 status in patients is advantageous for identifying potential beneficiaries of anti-HER-2 therapy. Artificial intelligence (AI)-assisted microscopy can integrate judgments on HER-2 status through extensive slide readings and reduce visual errors in human assessments. This study was performed to evaluate the practicality and feasibility of AI in evaluating HER-2 status in multiple regions of primary gastric cancer lesions.Methods A total of 264 postoperative specimens from patients with advanced gastric cancer were evaluated for HER-2 expression in the same region using two methods: visual assessment with a conventional light microscope and AI-assisted microscopy. The accuracy of AI in HER-2 evaluation in gastric cancer was evaluated. Additionally, using AI-assisted microscopy, HER-2 expression in other regions of the primary lesion in the gastric cancer patients was assessed. The higher HER-2 score between the two regions was used as the final interpretation result. Furthermore, the associations of HER-2 overexpression with clinicopathological features and postoperative survival of advanced gastric cancer patients were analyzed.Results There was no statistically significant overall difference in HER-2 scores among visual assessment, AI-assisted microscopy, and the gold standard (P>0.05), but AI-assisted microscopy showed higher consistency compared to visual assessment with the gold standard (κ=0.86 vs. κ=0.81, P<0.05). The results of HER-2 evaluation in two different regions of the primary lesion in gastric cancer using AI showed inconsistency in 55 cases, with a discordance rate of 20.8%. The HER-2 overexpression rate was 29.9% in region 1 and 31.0% in region 2. The comprehensive evaluation of HER-2 overexpression rate, taking the higher score between the two regions, was 35.2%. The HER-2 overexpression rate based on comprehensive evaluation of HER-2 expression in the two regions was higher than that of single-region assessment, but the difference was not statistically significant (P>0.05). Further analysis revealed that HER-2 heterogeneity in primary gastric cancer lesions was significantly associated with tumor differentiation and Lauren classification (both P<0.05). Survival analysis showed that the median 3-year overall survival was 23 months with a 3-year survival rate of 33.4% in HER-2 overexpressing advanced gastric cancer patients, while it was 29 months with a 3-year survival rate of 44.6% in HER-2 non-overexpressing advanced gastric cancer patients, and the difference was statistically significant (P<0.05).Conclusion AI is a practical and reliable tool for evaluating HER-2 expression in different regions of primary gastric cancer, enabling a more comprehensive and accurate assessment of HER-2 status in gastric cancer and improving the detection rate of HER-2 overexpression. HER-2 heterogeneous expression is more likely to occur in gastric cancer patients with moderate to low differentiation and non-intestinal type Lauren classification, and HER-2 overexpression may be a poor prognostic factor in resectable advanced gastric cancer patients.