The Role of Artificial Intelligence in the Management of Breast Cancer: A Review of Recent Advancements in Screening, Diagnosis, and Treatment
نویسندگان
1 Tehran Heart Center, Cardiovascular Disease Research Institute, Tehran University of Medical Sciences, Tehran, Iran
2 Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
3 Department of Internal Medicine, Breast health & cancer research center, School of Medicine, Hazrat-e Rasool General Hospital, Iran University of Medical Sciences, Teh-ran, Iran
4 Molecular and Medicine Research Center, Khomein University of Medical Sciences, Khomein, Iran
5 Student Research Committee, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
6 Department of Speech Therapy, School of Rehabilitation, Tehran University of Medical Sciences, Tehran, Iran
7 Clinical Research Development Unit, Ghaem Hospital, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
8 Digestive Oncology Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
9 Department of Medical Laboratory Sciences, Khomein University of Medical Sciences, Khomein, Iran
10 Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
11 Department of Education and Psychology, Shiraz University, Shiraz, Iran
doi
10.22034/ircmj.2025.492340.1725چکیده
Background and Objectives: Breast cancer is the most common cancer in women worldwide and poses a major public health challenge. Early detection and effective treatment are essential for better survival and outcomes. Traditional diagnostic and treatment approaches have evolved over time, and recent advancements in artificial intelligence (AI), including screening, diagnosis, treatment, and prognosis, have shown great promise in revolutionizing breast cancer care. Methods: The PubMed, Web of Science, and Scopus databases were searched using terms such as “artificial intelligence”, “machine learning”, “breast cancer”, and “breast neoplasm”, prioritizing recent publications. Results: AI’s ability to assist in medical imaging, predict cancer risk, and integrate with other screening methods is transforming traditional practices. Studies have demonstrated AI’s potential to match or even surpass that of human experts in cancer detection, increasing diagnostic accuracy and aiding in classifying tumor subtypes and grades. AI-driven predictive models are instrumental in predicting disease progression and treatment outcomes, allowing for more personalized therapeutic interventions. The adoption of AI in mammography has been shown to reduce radiologists’ workload while maintaining or improving diagnostic performance. AI models in ultrasound imaging enhance the differentiation of benign and malignant lesions, thus reducing unnecessary biopsies. In MRI, AI aids in precisely detecting and characterizing breast lesions, outperforming traditional methods. Conclusion: AI has significantly enhanced breast cancer care by improving diagnostic accuracy, aiding in early detection, and enabling personalized treatment. Its integration into screening and imaging processes is revolutionizing traditional practices, offering promising advancements in both clinical outcomes and healthcare efficiency.
