Matineh Behzadi, Anahita Azinfar, Hawraa Ibrahim Alshakarchi, Yeganeh Khazaei, Ibrahim Saeed Gataa, Gordon A. Ferns, Hamid Naderi, Amir Avan*, Hamid Fiuji and Masoud Pezeshki Rad Pages 1 - 10 ( 10 )
Breast cancer poses a significant global health challenge, necessitating improved diagnostic and treatment strategies. This review explores the role of artificial intelligence (AI) in enhancing breast cancer pathology, emphasizing risk assessment, early detection, and analysis of histopathological and mammographic data. AI platforms show promise in predicting breast cancer risks and identifying tumors up to three years before clinical diagnosis. Deep learning techniques, particularly convolutional neural networks (CNNs), effectively classify cancer subtypes and grade tumor risk, achieving accuracy comparable to expert radiologists. Despite these advancements, challenges, such as the need for high-quality datasets and integration into clinical workflows, persist. Continued research on AI technologies is essential for advancing breast cancer detection and improving patient outcomes.
Breast cancer, artificial intelligence, histopathological data, mammographic data.