Medical Informatics IV

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DOI :10.26650/B/T3.2024.40.023   IUP :10.26650/B/T3.2024.40.023    Full Text (PDF)

Advancement in Artificial Intelligence for Early Detection and Personalized Treatment of Breast Cancer

Murat Emeç

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.


Keywords: Artificial intelligence, breast cancer diagnosis, personalized treatment, image processing, medical imaging
DOI :10.26650/B/T3.2024.40.023   IUP :10.26650/B/T3.2024.40.023    Full Text (PDF)

Meme Kanseri̇ Erken Teşhi̇si̇ ve Ki̇şi̇selleşti̇ri̇lmi̇ş Tedavi̇si̇nde Yapay Zekâ Alaninda Geli̇şmeler

Murat Emeç

Meme kanseri, dünya genelinde kadınlarda en sık görülen kanser türlerinden biridir ve erken teşhis ve tedavi, başarı şansını önemli ölçüde artırmakta ve hastalığın seyrini olumlu yönde etkilemektedir. Son yıllarda, yapay zekâ (YZ) teknolojilerindeki hızlı gelişmeler meme kanseri tanı ve tedavisinde çok önemli bir rol oynamıştır. Bu makale, mevcut literatürde meme kanserinin erken teşhisi ve kişiselleştirilmiş tedavisinde YZ tabanlı yaklaşımların önemli rolünü kapsamlı bir şekilde gözden geçirmeyi amaçlamaktadır. Bu alandaki temel çalışmaların bir derlemesi olarak, bu makalede meme kanseri teşhisine katkıda bulunan çeşitli yapay zekâ algoritmaları ve yöntemleri tartışılmaktadır. Derin öğrenme algoritmaları, özellikle mamografi ve diğer görüntüleme tekniklerine uygulandığında, erken teşhis için yüksek hassasiyet ve özgüllük sağlamada kayda değer bir başarı elde edilmiştir. Ayrıca, bilgisayar destekli tanı sistemleri radyologları tanı sürecinde desteklemekte ve doğruluk oranlarını artırmaktadır. Bu çalışmanın bir diğer önemli odak noktası da yapay zekânın kişiselleştirilmiş tedavideki rolüdür. Genetik analiz ve moleküler profillemeye dayalı olarak, kanser hastaları için tedavi yaklaşımlarının özelleştirilmesi, hastalığın ilerlemesini durdurma ve yan etkileri en aza indirme şansını artırabilir. Yapay zekâ, kapsamlı veri analizine yardımcı olarak, genetic mutasyonları tespit ederek ve ilaç yanıtlarını tahmin ederek kişiselleştirilmiş tedavi süreçlerine entegre edilmektedir. Bununla birlikte, meme kanseri teşhisi ve tedavisinde yapay zekânın tam potansiyelini anlamak ve klinik uygulamalar için hazır hale getirmek bazı zorluklar ve engeller ortaya çıkarmaktadır. Veri gizliliği ve güvenlik endişeleri, algoritmanın açıklanabilirliği ve kabulü çözüm gerektirmektedir. Sonuç olarak, bu makalede incelenen literatür, meme kanseri teşhisi ve tedavis I için YZ tabanlı yaklaşımların potansiyelini vurgulamaktadır. YZ, meme kanserli hastaların yaşam kalitesini ve hayatta kalma oranlarını iyileştirmeyi amaçlayan erken teşhis ve kişiselleştirilmiş tıpta önemli bir araç olarak istikrarlı bir şekilde ilerlemektedir. Bununla birlikte, bu teknolojilerin güvenilirliğini ve etkinliğini belirlemek için daha fazla araştırma ve klinik çalışma yapılması büyük önem taşımaktadır.


Keywords: Yapay zekâ, meme kanseri teşhisi, kişiselleştirilmiş tedavi, görüntü işleme, tıbbi görüntüleme

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