Akademik Veri Yönetim Sistemi
MEDICAL INFORMATICS IV, Nilgün Bozbuğa,Sevinç Gülseçen, Editör, Istanbul University Press, İstanbul, ss.357-382, 2024
Breast cancer is one of the most common types of cancer in women worldwide, and early diagnosis and treatment significantly improve the chances of success and positively impact the disease course. In recent years, rapid advancements in artificial intelligence (AI) technologies have played a crucial role in breast cancer diagnosis and treatment. This article aims to comprehensively review the significant role of AI-based approaches in the early detection and personalized treatment of breast cancer in the existing literature. As a compilation of essential studies in this field, this article discusses various artificial intelligence algorithms and methods contributing to breast cancer diagnosis. Deep-learning algorithms, mainly when applied to mammography and other imaging techniques, have achieved remarkable success in providing high sensitivity and specificity for early detection. Additionally, computer-aided diagnosis systems support radiologists in the diagnostic process and enhance accuracy rates. Another key focus of this abstract is the role of AI in personalized treatment. Based on genetic analysis and molecular profiling, customization of treatment approaches for cancer patients can enhance the chances of halting disease progression and minimizing side effects. AI is integrated into personalized treatment processes by assisting with extensive data analysis, detecting genetic mutations, and predicting drug responses. However, comprehending the full potential of AI in breast cancer diagnosis and treatment and making it readily available for clinical applications present some challenges and barriers. Data privacy and security concerns, algorithm explainability, and acceptance require resolution. In conclusion, the literature reviewed in this article emphasizes the potential of AI-based approaches for breast cancer diagnosis and treatment. AI is steadily progressing as a crucial tool in early detection and personalized medicine, aiming to improve the quality of life and survival rates of patients with breast cancer. Nonetheless, further research and clinical studies are of utmost importance to establish the reliability and efficacy of these technologies.