Breast Cancer Diagnosis Reimagined: The Rise of Biopsy 2.0 and Artificial Intelligence-A Review

Breast cancer continues to hold the title of most common cancer among females around the world as it remains a significant contributor to cancer-related deaths. The speed and exactness of diagnosis serve as critical determiners for improved results yet standard biopsy procedures such as Core Needle Biopsy (CNB) and Fine Needle Aspiration (FNA) need better specimen acquiring techniques because of their irregular interpretations and slow diagnostic processes. Biopsy 2.0 emerged through artificial intelligence technologies to provide better diagnostic accuracy by integrating AI systems during biopsy evaluation but also guarantees efficient personalized outcomes. AI applications in breast cancer diagnosis span multiple domains. CNN-based deep learning models achieve diagnosis performance like experienced pathologists when they analyze WSIs to diagnose tumors and determine their staging and identify receptor activities. AI systems support current liquid biopsy techniques by analyzing circulating tumor DNA (ctDNA) together with Circulating Tumor Cells (CTCs), which enables early disease detection and proper treatment monitoring as well as prognosis of recurrence. Healthcare providers obtain better risk assessments and create person-specific treatment plans through AI predictive models, which handle clinical information with molecular data. The adoption of new biosystems for clinical practice encounters present barriers due to data prejudice and regulatory constraints, and interpretation barriers, which delay widespread implementation. This investigation explores current developments with clinical importance of AI-based breast cancer biopsy approaches alongside an assessment of Biopsy 2.0 as a potential system to revolutionize oncological testing and prediction.