Abstract Background: Characterization of breast micro calcification on mammography is challenging. Artificial intelligence (AI) could be of great help in differentiating benign and malignant breast micro calcification. Aim of Study: The aim of this study is to discover the role of artificial intelligence in the assessment of mammographi-cally-detected breast micro calcification. Patients and Methods: Forty-four female patients between the ages of 32-75 years (mean age, 48.95 years), with breast micro calcification on mammography were included in this study. The AI scores of micro calcifications were detected after uploading the mammography images on the AI worksta-tion. Core-needle biopsies were performed for all patients for histopathological correlation. Results: This study included 44 patients with breast micro calcification detected on mammography. This micro calcifi-cation was assigned BIRADS 2 to 6 according to ACR-BIRADS lexicon. A cut-off value of 95% for AI score was used for differentiation between benign and malignant micro-calcificaton. Conclusion: Artificial intelligence is an effective tool in the characterization of breast microcalcification, however, more research is needed to set the optimal cut-off value for reducing false negative cases.