Personalized medicine is needed since standard breast cancer diagnosis and treatment are not always exact or customized. To revolutionize breast cancer diagnosis, this paper examines the clinical utility of artificial intelligence (AI), specifically deep learning (DL). The accuracy with which AI can analyze vast medical databases—genetic data, clinical background, and images— enabling improved accuracy in diagnosis, staging, and treatment planning through integration of multimodal data.

Recent studies have shown that DL algorithms have already attained 94–98 % diagnostic accuracies in recognizing breast cancer subtypes from histopathological images, against the overall accuracy of approximately 88 % of an average human pathologist. Additionally, CNNs have achieved AUC values of >0.95 in distinguishing between malignant and benign breast lesions based on mammography, substantiating the fact that AI enhances the diagnostic accuracy with a significant improvement.

The study highlights a few case studies, such as fibroadenoma, invasive lobular carcinoma (ILC), lobular carcinoma in situ (LCIS), and sclerosing adenosis (SAD), to illustrate how DL algorithms outperform human experts in early detection and correct diagnosis. Incorporating DL into personalized medicine not only guarantees more specialized and effective treatment plans but also guarantees a future where every patient with breast cancer will be treated with personalized care. Other than referencing AI's revolutionizing impact on breast cancer screening, this research offers an unassailable case for adopting AI and signals a new epoch of accurate, patient-centered medicine.