Recent AI Advances in Digital Breast Pathology: Models, Explainability, and Applications

Key Details

• 1The review summarizes foundational AI concepts such as neural networks, deep learning, and multimodal models in breast pathology.
• 2Current clinical applications include lymph node metastasis detection, Nottingham grading, biomarker quantification, and tumor classification.
• 3Explainable AI and model transparency are highlighted as critical for real-world clinical adoption.
• 4Recent developments include multimodal foundation models for comprehensive disease characterization and patient risk stratification.
• 5Major implementation challenges cover data quality, bias, regulatory requirements, cost, and workflow integration.
• 6AI integration is seen as augmenting, not replacing, pathologists to enhance diagnosis and patient care.