Tıpta ve Veteriner Tıpta Yapay Zeka

Yıl 2022, Cilt: 1 Sayı: 1, 33 - 39, 26.12.2022

Özge Sevinç Korkmaz Akar Yakup Yıldırım

Öz

İnsan zekasının makineler tarafından taklit edilmesi hayali ile ortaya konulan yapay zeka fikri, ilk bakışta homojenik bir kavram gibi algılansa da aslında farklı sistemlerin farklı amaçlarla çalıştığı birden çok türü mevcuttur. Uzman sistemler, bulanık mantık ve genetik algoritmalar gibi zekanın farklı özelliklerini farklı bir modelleme ile taklit etmeyi hedefleyen türleri vardır. Bu alandaki en çarpıcı ilerleme ise; makinelerin insan zekasına en çok yaklaştığı alan olan ve biyolojik sinir ağlarını taklit eden yapay sinir ağlarının pratiğe dökülmesi ile olmuştur Teknolojinin sağlık alanında aktif kullanımı canlıların hayatını koruma ve uzatma, hayat kalitesini iyileştirme şansını daha da ileriye taşımaktadır. Yapay zekanın sağlık alanında kullanılması ile canlıların hayat ve sağlık standartlarının iyileştirilmesine yönelik etkisinin olumlu yönde olacağı yalın bir gerçektir. Tıpkı insan zekası gibi yapay zekanın da etkin kullanımı sağlık alanında yeni atılımları da beraberinde getirecektir Bu derlemede yapay zekanın insan ve hayvan sağlığı alanında kullanımı ve uygulamaları hakkında bilgi verilmesi amaçlanmıştır.

Anahtar Kelimeler

Yapay Zeka, Biyoteknoloji, Tıp, Veteriner Tıp

Artificial Intelligence in Medicine and Veterinary Medicine

Öz

Even though the very idea of artificial intelligence, which was put forward with the imagination of imitating human intelligence by machines, is perceived as a homogeneous and simple concept, there are different types of system working and aiming for different purposes including expert systems, fuzzy logic and genetic algorithms. The one of the most drastic progresses in this field is implementation of artificial neural networks as the closest point to biological neural networks of human intelligence.
The use of artificial intelligence in medicine enables the opportunity to protect and prolong the life and improve the quality of life even further. With the help of developed artificial intelligence applications, health management and monitoring will improve both health professionals and patients’ life significantly. In other words, with the efficient and widespread use of artificial intelligence in medicine, there will be plenty of benefits on health.
In this review, it is aimed to give information about the history and the progress of artificial intelligence and updated applications in both human and veterinary medicine.

Anahtar Kelimeler

Artificial Intelligence, Biotechnology, Medicine, Veterinary Medicine

Kaynakça

• Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A., & Arshad, H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11), e00938.
• Ahmed, F.E. (2005). Artificial neural networks for diagnosis and survival prediction in colon cancer. Molecular cancer 4(1): 1-12. https://doi.org/10.1186/1476-4598-4-29
• Akilli, A., Atil, H., Takma, C., & Ayyilmaz, T. (2016). Fuzzy logic-based decision support system for dairy cattle. Kafkas Univ Vet Fak Derg. 22 (1): 13-19. doi:10.9775/kvfd.2015.13516
• Amisha, P.M., Pathania, M., & Rathaur, V.K. (2019). Overview of artificial intelligence in medicine. Journal of family medicine and primary care 8(7): 2328. doi: 10.4103/jfmpc.jfmpc_440_19
• Avignon, D., Farnir, F., Iatridou, D., Iwersen, M., Lekeux, P., Moser, V., Saunders, J., Schwarz, T., Sternberg-Lewerin, S. & Weller, R. (2020). Report of the Eccvt Expert Working Group on the Impact of Digital Technologies & Artificial Intelligence in Veterinary Education and Practice.
• Banner, M. J., Euliano, N. R., Brennan, V., Peters, C., Layon, A. J., & Gabrielli, A. (2006). Power of breathing determined noninvasively with use of an artificial neural network in patients with respiratory failure. Critical care medicine, 34(4), 1052-1059. doi: 10.1097/01.CCM.0000206288.90613.1C
• Baykal, N., & Beyan, T. (2004). Bulanık Mantık, Uzman Sistemler ve Denetleyiciler.10. Baskı, Ankara: Bıçaklar Kitabevi 23-411.
• Bregman, R. (2017). Utopia for realists: How we can build the ideal world. New York: Little, Brown and Company
• Caetano, M.A., de Souza, J.F., & Yoneyama, T. (2008). Optimal medication in HIV seropositive patient treatment using fuzzy cost function. American Control Conference. 11-13 Haziran Seattle, Washington, USA 2227-2232. doi: 10.1109/ACC.2008.4586823
• Cokar, M. (2019). Sağlıkta Yapay Zekâ, Kuramsal Çerçeve ve Etik [Turkish]. In: Bulut, M., Dilmen, N., Esmer, G.B., Gezer, M., Selçukcan Erol, Ç., Türker Şener, L. (Ed), Sağlık Bilimlerinde Yapay Zekâ, Birinci Baskı, İstanbul: Çağlayan Kitabevi ve Eğitim Çözümleri Ticaret A.Ş. 211-231.
• Duraipandian, S., Zheng, W., Ng, J., Low, J. J., Ilancheran, A., & Huang, Z. (2011). In vivo diagnosis of cervical precancer using Raman spectroscopy and genetic algorithm techniques. Analyst, 136(20), 4328-4336. https://doi.org/10.1039/C1AN15296C
• Elveren, E., & Yumuşak, N. (2011). Tuberculosis disease diagnosis using artificial neural network trained with genetic algorithm. Journal of medical systems 35(3): 329-332. https://doi.org/10.1007/s10916-009-9369-3
• Ezanno, P., Picault, S., Beaunée, G., Bailly, X., Muñoz, F., Duboz, R., Monod, H. & Guégan, J. F. (2021). Research perspectives on animal health in the era of artificial intelligence. Veterinary research, 52(1), 1-15. https://doi.org/10.1186/s13567-021-00902-4
• Fogel, D.B., Wasson, III E.C., & Boughton, E.M. (1995). Evolving neural networks for detecting breast cancer. Cancer letters 96(1): 49-53. https://doi.org/10.1016/0304-3835(95)03916-K
• Fraiwan, M.A., & Abutarbush, S.M. (2020). Using Artificial Intelligence to Predict Survivability Likelihood and Need for Surgery in Horses Presented With Acute Abdomen (Colic). Journal of Equine Veterinary Science 90, 102973. https://doi.org/10.1016/j.jevs.2020.102973
• Fuentes, S., Viejo, C. G., Tongson, E., & Dunshea, F. R. (2022). The livestock farming digital transformation: implementation of new and emerging technologies using artificial intelligence. Animal Health Research Reviews, 1-13. https://doi.org/10.1017/S1466252321000177
• Gezer, M. (2019). Yapay Zekâ ve Tarihçesi. In: Bulut, M., Dilmen, N., Esmer, G.B., Gezer, M., Selçukcan Erol, Ç., Türker Şener, L. Sağlık Bilimlerinde Yapay Zekâ, Birinci Baskı, İstanbul: Çağlayan Kitabevi ve Eğitim Çözümleri Ticaret A.Ş. 1-14.
• Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California management review 61(4): 5-14. https://doi.org/10.1177/000812561986492
• Hempstalk, K., McParland, S., & Berry, D.P. (2015). Machine learning algorithms for the prediction of conception success to a given insemination in lactating dairy cows. Journal of dairy science 98(8): 5262-5273. https://doi.org/10.3168/jds.2014-8984
• Huang, D.H., & Chueh, H.E. (2020). Chatbot usage intention analysis: Veterinary consultation. Journal of Innovation & Knowledge 6(3): 135-144. https://doi.org/10.1016/j.jik.2020.09.002
• Karnan, M., & Thangavel, K. (2007). Automatic detection of the breast border and nipple position on digital mammograms using genetic algorithm for asymmetry approach to detection of microcalcifications. Computer methods and programs in biomedicine 87(1): 12-20. https://doi.org/10.1016/j.cmpb.2007.04.007
• Kaul, V., Enslin, S., & Gross, S.A. (2020). The history of artificial intelligence in medicine. Gastrointestinal endoscopy 92(4): 807-812. https://doi.org/10.1016/j.gie.2020.06.040
• Kulikowski, C.A. (2019). Beginnings of artificial intelligence in medicine (AIM): computational artifice assisting scientific inquiry and clinical art–with reflections on present aim challenges. Yearbook of medical informatics 28(1): 249. doi: 10.1055/s-0039-1677895
• Küçükönder, H., Üçkardeş, F., & Narinç, D. (2014). Hayvancılık alanında bir veri madenciliği uygulaması: Japon bıldırcını yumurtalarında döllülüğe etki eden bazı faktörlerin belirlenmesi. Kafkas Üniversitesi Veteriner Fakültesi Dergisi 20(6): 900-908. doi: 10.9775/kvfd.2014.11353
• Letheren, K., Russell-Bennett, R., & Whittaker, L. (2020). Black, white or grey magic? Our future with artificial intelligence. Journal of Marketing Management. 36(3-4): 216-232. https://doi.org/10.1080/0267257X.2019.1706306
• Li, S., Wang, Z., Visser, L. C., Wisner, E. R., & Cheng, H. (2020). Pilot study: application of artificial intelligence for detecting left atrial enlargement on canine thoracic radiographs. Veterinary radiology & ultrasound, 61(6), 611-618. https://doi.org/10.1111/vru.12901
• Mecocci, P., Grossi, E., Buscema, M., Intraligi, M., Savarè, R., Rinaldi, P., Cherubini A., & Senin, U. (2002). Use of artificial networks in clinical trials: a pilot study to predict responsiveness to donepezil in Alzheimer's disease. Journal of the American Geriatrics Society, 50(11), 1857-1860. https://doi.org/10.1046/j.1532-5415.2002.50516.x
• Nabiyev, V.V. (2005). Yapay Zekâ; Problemler, Yöntemler, Algoritma [Turkish]. 2. Baskı, Ankara: Seçkin Yayıncılık 25-222.
• Ng, A. (2011). Sparse autoencoder. CS294A Lecture notes 72: 1-19.
• Nicholson, C. (2020). A beginner's guide to neural networks and deep learning. https://wiki.pathmind.com/neural-network. 2020 Accessed: 20.09.2021.
• Ozsahin, D. U., Uzun, B., Ozsahin, I., Mustapha, M. T., & Musa, M. S. (2020). Fuzzy logic in medicine. In Biomedical Signal Processing and Artificial Intelligence in Healthcare (pp. 153-182). Academic Press. https://doi.org/10.1016/B978-0-12-818946-7.00006-8
• Persi Pamela, I., & Gayathri, P. (2013). A fuzzy optimization technique for the prediction of coronary heart disease using decision tree. International Journal of Engineering and Technology 5(3): 2506-2514.
• Ruffle, J.K., Farmer, A.D., & Aziz, Q. (2019). Artificial intelligence-assisted gastroenterology—promises and pitfalls. American Journal of Gastroenterology 114(3): 422-428. doi: 10.1038/s41395-018-0268-4
• Takma, C., Atil, H., & Aksakal, V. (2012). Çoklu doğrusal regresyon ve yapay sinir ağı modellerinin laktasyon süt verimlerine uyum yeteneklerinin karşılaştırılması. Kafkas Üniversitesi, Veteriner Fakültesi Dergisi 18(6): 941-944. doi:10.9775/kvfd.2012.6764
• Turing, A. (2009). Computing machinery and intelligence. Parsing the Turing Test 23-65. https://doi.org/10.1007/978-1-4020-6710-5_3
• Weiss, S., Kulikowski, C.A., & Safir, A. (1978). Glaucoma consultation by computer. Computers in Biology and Medicine 8(1): 25-40. https://doi.org/10.1016/0010-4825(78)90011-2
• Yesil, Y. (2019). Sağlıkta Yapay Zekâ ve Gerçeklik Teknolojileri. In: Bulut, M., Dilmen, N., Esmer, G.B., Gezer, M., Selçukcan Erol, Ç., Türker Şener, L. Sağlık Bilimlerinde Yapay Zekâ, Birinci Baskı, İstanbul: Çağlayan Kitabevi ve Eğitim Çözümleri Ticaret A.Ş. 171-181.
• Zadeh, L.A. (2008). Is there a need for fuzzy logic? Information sciences 178(13): 2751-2779. https://doi.org/10.1016/j.ins.2008.02.012
• Zhou, L., & Sordo, M. (2020). Expert systems in medicine. In: Xing L, Giger ML, Min JK (Ed), Artificial Intelligence in Medicine Technical Basis and Clinical Applications, Cambridge, Massachusetts: Academic Press 75-100.
• Zuraw, A., & Aeffner, F. (2022). Whole-slide imaging, tissue image analysis, and artificial intelligence in veterinary pathology: An updated introduction and review. Veterinary Pathology, 59(1), 6-25. https://doi.org/10.1177/03009858211040484