Roshan Sah, Tanya Jain, Prachee Raje Bisht, Anjali Saini, Manish Pal Singh
Breast cancer is still one of the most significant global contributors to cancer-related deaths. Artificial intelligence (AI) applications in imaging, diagnostic imaging and assistance, systemic treatment and personalization, and predictive modeling are all part of the fast-growing field of breast cancer care. This review examined the many applications of AI in cancer care, including prediction, diagnostic work-up, staging, early diagnosis, and therapy response. AI's contributions to radiology include risk assessment models, tomosynthesis-enhanced mammography, and other imaging methods, including MRI and ultrasound. In pathology, AI can improve marker quantification and histological diagnosis, as well as prognostic models, including models for predicting genetic mutations. Realising the potential of AI, however, will not be without challenges that may limit ubiquity in clinical practice. Our review highlights the potentially transformational but presently limited ability of AI to improve breast cancer care. It urges targeted research to eliminate these obstacles and maximize the application of AIsupported solutions to those that impede clinical adoption in the real world. We have been able to demonstrate how AI may enhance diagnostic precision, customize treatment options, and have a favorable impact on patient outcomes from detection to treatment and follow-up care by methodically recording its involvement in radiology and pathology. The effectiveness of AI's influence on breast cancer treatment hinges on cooperation and regulatory compliance.