Abstract
Kanser, insidans ve mortalite oranları ile insan yaşamını etkilediği bilinen, multifaktöriyel bir hastalık grubudur. Yapay zeka ve kanser tedavisinde yeni stratejilerin geliştirilmesi, hekimlerin hastaya özel optimize tedavi uygulayabilmesinde, hem fiziksel hem de psikolojik güçlüklerin aşılmasında, hastalığın tekrarının ve yayılımının önlenmesinde büyük öneme sahiptir. Güncel gelişmelerle eşgüdümlü olarak birbirine entegre olan sağlık ve yapay zeka alanı sayesinde çağımızın büyük sorunlarında başta gelen kanserin, tanı, tedavi ve prognozunda büyük ilerlemeler kaydedilmiş ve kaydedilmeye devam etmektedir.
Keywords
References
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Abstract
Cancer is a multifactorial group of diseases that are known to affect human life with incidence and mortality rates. Artificial Intelligence and the development of new strategies in cancer treatment are of great importance in helping physicians apply optimized treatment tailored to the patient, overcoming both physical and psychological difficulties, and preventing the recurrence and spread of the disease. Thanks to the field of health and Artificial Intelligence, which are integrated with current developments in coordination with each other, great advances have been made and continue to be made in the diagnosis, treatment and prognosis of cancer, one of the major problems of our age.
References
- 1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021: 71: 209-249.
- 2. Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229-263.
- 3. Huang S, Yang J, Fong S, Zhao Q. Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges. Cancer Lett. 2020;471:61-71.
- 4. Hu, C., Liu, X., Zeng, Y. et al. DNA methyltransferase inhibitors combination therapy for the treatment of solid tumor: mechanism and clinical application. Clin Epigenet, 2021; 13, 166.
- 5. Wong D, Yip S. Machine learning classifies cancer. Nature. 2018; 555(7697):446-447.
- 6. Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI. Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J. 2014;13:8-17.
Details
| Primary Language | English |
|---|---|
| Subjects | Cancer Diagnosis, Cancer Therapy (Excl. Chemotherapy and Radiation Therapy), Predictive and Prognostic Markers |
| Journal Section | Letter to Editor |
| Authors | |
| Early Pub Date | July 4, 2024 |
| Publication Date | |
| Submission Date | May 11, 2024 |
| Acceptance Date | June 13, 2024 |
| Published in Issue | Year 2024 Volume: 5 Issue: 2 |
