Breast cancer remains a leading cause of morbidity and mortality among women globally. As a pervasive and heterogeneous cancer type, correct identification of BC grade is of paramount importance for tailored targeted treatment and reduction of the death rate, with imaging modalities such as mammography, ultrasound, and MRI playing central roles. However, diagnostic accuracy is often challenged by factors like breast density, radiologist variability, and health disparities. These challenges have motivated the integration of AI to enhance risk assessment, diagnosis, and treatment planning in breast cancer care.
¶ AI and Breast Cancer
AI-driven technologies are playing a growing role in improving breast cancer detection, diagnosis, and treatment planning. These algorithms can evaluate mammograms and other imaging data to detect subtle patterns associated with cancer, enabling earlier and more precise diagnoses. For instance, AI-based risk assessment models that leverage imaging data have demonstrated strong performance in accurately predicting breast cancer risk. Convolutional Neural Networks (CNNs) have shown strong performance in identifying malignant lesions from mammograms, often rivaling or exceeding expert-level performance.
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Despite advances, disparities persist in breast cancer outcomes, particularly among Black women who experience higher mortality rates and are more likely to be diagnosed with aggressive subtypes. These disparities are compounded by factors such as delayed diagnosis, access barriers, variations in tumor biology, and unequal healthcare access. Dense breast tissue also contributes to reduced screening sensitivity, disproportionately affecting younger and minority women.
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¶ Intersection of AI, Health Disparity, and Breast Cancer
Integrating AI into breast cancer care offers an opportunity to address disparities in equity, diagnostic accuracy, and early detection. One promising direction is the use of multimodal AI models that combine imaging data with clinical and demographic variables (such as age, race, and family history) to personalize screening and enhance risk prediction. AI models trained on diverse datasets that include underrepresented groups are more capable of identifying patterns across different populations, improving generalizability and reducing bias in breast cancer prediction.
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