Clinical translation of artificial intelligence in breast cancer imaging diagnosis
SHI Jing
ZHANG Yue
ZHANG Qing
Abstract：Breast cancer is the malignant tumor with the highest incidence in women worldwide.Therefore,early detection and early diagnosis are the keys to prolong survival,but there are limitations in traditional imaging diagnostic methods.In recent years,artificial intelligence(AI)technology has significantly improved the accuracy and efficiency of breast cancer imaging diagnosis through deep learning and image processing.In breast ultrasound,mammography,Breast MRI and emerging imaging technologies,AI can promote the development of medical imaging through lesion detection,classification,image enhancement,risk prediction and clinical decision support.However,the clinical translation of AI still faces challenges such as data standardization,algorithm generalization,and ethical compliance.In the future,it is necessary to strengthen multi-center cooperation,promote technological innovation,and improve ethical regulations,to ensure that it can truly meet clinical needs,so as to promote the intelligence,precision and universality of breast cancer diagnosis and treatment.This paper provides a systematic review of the comparative advantages and limitations of AI versus conventional imaging methods in the diagnosis,treatment,and prognosis prediction of breast diseases.It explores feasible pathways for clinical translation and future development directions,while also offering insights into the prospective applications of AI in breast disease diagnosis and treatment.
Keywords：artificial intelligencebreast cancermedical imagingdeep learning
Publication Date：2025-11-20
Online Publishing Date：2025-12-16(First online date of this platform, not the publication date of the document)
Pages：6( 1415-1420 )