AI integration framework for precision breast cancer care from imaging to treatment

A review outlines an AI framework spanning breast imaging, digital pathology, multi-omics, and treatment decisions, but cautions that bias, limited datasets, and poor interpretability impede clinical adoption.

• Narrative review proposing a framework that unifies AI applications across screening, diagnosis, treatment prediction, and surgical planning for breast cancer.
• Highlights unresolved challenges: algorithmic bias, dataset comprehensiveness, and model interpretability, which limit real-world translation.
• No original data; purely a synthesis of existing literature and conceptual framework.

RadPigeon summaries are original and for information only. They are not clinical advice.