Artificial Intelligence and Its Areas of Use in Healthcare

Abstract

Artificial intelligence (AI) is computer systems that can perform tasks that require human intelligence. It consists of data based on machine learning, deep learning and artificial neural networks. AI; with the increase in data collection and the ability to store large numbers of data, its use in the field of health has increased. It has been increasing rapidly recently. AI is being used more and more frequently with its features that help physicians in diagnosis, treatment planning, prognosis prediction and application of treatments. In this review, it is aimed to specify AI and its areas of use in the healthcare system.

Keywords

• artificial intelligence
• deep learning
• dentistry
• health

References

1. Agrawal, P. & Nikhade, P. Artificial intelligence in dentistry: Past, present, and future. cureus. (2022) ,14(7), doi: 10.7759/cureus.27405.
2. Akdoğan, C. & Özdemir, H. (2024). Yapay zeka’nın diş hekimliği pratiğine kazanımları. Türk Diş Hekimleri Araştırma Dergisi, 2(3), 278-287.
3. Amasya, H., Yıldırım, D., Aydoğan, T., Kemaloğlu, N., & Orhan, K. (2020). Cervical vertebral maturation assessment on lateral cephalometric radiographs using artifical intelligence: comparison of machine leranig classifier models. Dentomaxillofac Radiology, 49(5).
4. Atalay, M. & Çelik, E. (2017). Büyük veri analizinde yapay zeka ve makine öğrenmemesi uygulamaları-artificial intelligence and machine learning applications in big data analysis. Mehmet Akit Ersoy Üniversitesi Sosyal Bilimleri Enstitüsü Dergisi, 9(22), 155-172.
5. Ding, H., Wu, J., Zhao, W., Matinlinna, J., Burrow, M., & Tsoi, J. (2023). Artificial intelligence in dentistry, A review. Frontiers in Dental Medicine, 4(1), 1-10.
6. Doğan, F. & Türkoğlu, İ. (2019). Derin öğrenme modelleri ve uygulama alanlarına ilişkin bir derleme, Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 10(2), 409-445.
7. Duman, S., Yılmaz, E. F., Eşer, G., Çelik, Ö., Bayrakdar, I. S., Bilgir, E., & Orhan, K. (2023). Detecting the presence of taurodont teeth on panoramic radiographs using a deep learning-based convolutional neural network algorithm. Oral Radiology, 39(1), 207-214.
8. Hiraiwa, T., Ariji, Y., Fukuda, M., Kise, Y., Nakata, K., Katsumata, A., & Ariji, E. (2019). A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography. Dentomaxillofacial Radiology, 48(3), 1-3.

9. Hoşgör, H. & Güngördü, H. (2022). Sağlıkta yapay Zekanın kullanım alanları üzerine nitel bir araştırma. Avrupa Bilim ve Teknoloji Dergisi, 1(35), 395-407.
10. Hwang, J. J., Jung, Y. H., Cho, B. H., & Heo, M. S. (2019). An Overviwe of deep learning in the field of dentistry. Imaging science in dentistry, 49(1), 1-7.
11. Katabağ, M. (2021) Ahlaki değerlerin kodlanabilmesi bağlamında yapay zeka etiğine kurumsal bir bakış. TRT Akademisi, 13(6), 748-767.
12. Kaya, E., Gunec, H. G., Gokyay, S. S., Kutal, S., Gulum, S., & Ateş, H. F. (2022). Proposing a CNN method for primary and permanent tooth detection and enumeration on pediatric dental radiographs. Journal of Clinical Pediatric Dentistry, 46(4), 293-298.
13. Köroğlu, Y. (2017). Yapay zekanın teorik ve pratik sınırları. Boğaziçi Üniversitesi Yayınevi,1-10.
14. Özkesici, M. Y. & Yılmaz S. (2021). Oral ve maksillofasiyal radyoloji’de yapay zeka. Sağlık Bilimleri Dergisi, 30(3), 346-351.
15. Saghiri, M. A., Asgar, K., Boukani, K., Lotfi, M., Aghili, H., Delvarani, A., & Garcia-Godoy, F. (2021a). A new approach for locating the minor apical foramen using an artifical neural network. International endodotic journal, 45(3), 257-265.
16. Saghiri, M. A., Garcia-Godoy, F., Gutmann, J. L., Lotfi, M., & Asgar, K. (2021b). The reliability of artificial neural network in locating minor apical foramen: a cadaver study. Journal Of endodontics, 38(8), 1130-1134.
17. Schönewolf, J., Meyer, O., Engels, P., Schlickenrieder, A., Hickel, R., Gruhn, V., & Kühnisch, J. (2022). Artificial intelligence-based diagostics of molar-incisor-hypomineralization(MIH) on intraoral photographs. Clinical Oral Investigation, 26(9), 5923-5930.
18. Setzer, F. C., Shi, K. J., Zhang, Z., Yan, H., Yoon, H., Mupparapu, M., & Li, J. (2020). Artificial intelligence for the computer-aided detection of periapical lesions in cone-beam computed tomographic images. Journal of endodontics, 46(7), 987-993.
19. Taş, H. Y. (2018). Dördüncü sanayi devrimi’nin (Endüstri 4.0) çalışma hayatına ve istihdama muhtemel etkileri, OPUS Uluslararası Toplum Araştırmaları Dergisi, 16(9), 1817-1836.
20. Türk, E. (2021). Gömülü mandibular üçüncü molar dişlerin pozisyon özelliklerinin yapay zeka ile değerlendirilmesi. (Uzmanlık Tezi). Süleyman Demirel Üniversitesi, Isparta.
21. Uthoff, R. D., Song, B., Sunny, S., Patrick, S., Sureshi, A., Kolur, T., & Wilder-Smith, P. (2018). Point-of-care, smartphone-based, dual-modality, dual-view- oral cancer screening device with neurol network classification for low-resource communities. PLoS One, 13(12), 1-21.
22. Yılmaz, A. (2021). Derin öğrenme: İstanbul