A Bibliometric Analysis of Graduate Theses on the Use of Artificial Intelligence Methods in the Field Of Healthcare (2015-2022)
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Keywords:Artificial Intelligence, Bibliometric Analysis, Graduate Theses, Healthcare, Medicine
In the light of technological developments, great changes have recently occurred in the field of health. These changes have accelerated with artificial intelligence, which we have frequently heard about recently. With the use of artificial intelligence in the field of health, significant gains have started to be achieved in the processes of patients responding to treatment in health institutions. From this point of view, it is possible to say that developments related to artificial intelligence in health institutions will increase more in the future and take more place in human life. Studies on artificial intelligence in the field of health have increased significantly, especially in the last five years. This significant increase has been a matter of curiosity about the methods by which artificial intelligence studies in the field of health are analyzed. With this study, it is aimed to bibliometrically examine the theses published in the Council of Higher Education (YÖK) Presidency Thesis Center on artificial intelligence in the field of health with the determined parameters. The study population consists of 130 master's and doctoral theses published between 01.01.2015 - 31.12.2022. The theses were evaluated in terms of bibliometric parameters according to type, year, language of publication, gender of authors, advisor title, province, university, institute, department, research method, data collection method, page range and keywords used. This study will fill an important gap in the literature on the concept of artificial intelligence in the field of health at the national level and will provide preliminary information to researchers who will conduct studies on artificial intelligence.
Ahmad, J., Farman, H., & Jan, Z. (2019). Deep learning methods and applications. In Deep learning: convergence to big data analytics (pp. 31-42). Springer, Singapore.
Akalin, B., & Veranyurt, Ü. (2021). Sağlık hizmetleri ve yönetiminde yapay zekâ. Acta Infologica, 5(1), 231-240.
Atkinson, M. (1979). Artificial ıntelligence and natural man: Margaret A. Boden. Philosophical Quarterly, 29(116), 278-281.
Buchanan, C., Howitt, M. L., Wilson, R., Booth, R. G., Risling, T., Bamford, M. (2021). Predicted Influences of Artificial Intelligence on Nursing Education: Scoping Review. JMIR nursing, 4(1), e23933. https://doi.org/10.2196/23933.
Çilhoroz, Y., Işık, O. (2021). Yapay Zekâ: Sağlık Hizmetlerinden Uygulamalar. Ankara Hacı Bayram Veli Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 23(2), 573-588.
Davidson, L., Boland, M.R. (2021). Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes. Briefings in Bioinformatics, 22(5), 1-29. https://doi.org/10.1093/bib/bbaa369.
Eroğlu İ. (2010). Binalarda enerji yönetimi ve enerji kullanım verimliliğini etkileyen faktörlerin yapay zekâ teknikleri ile analizi. Sakarya Üniversitesi Fen Bilimleri Enstitüsü, Yüksek Lisans Tezi, Sakarya.
Hayadi, B. H., Bastian, A., Rukun, K., Jalinus, N., Lizar, Y., & Guci, A. (2018). Expert system in the application of learning models with forward chaining method. Int. J. Eng. Technol, 7(2.29), 845.
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., ... & Wang, Y. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology, 2(4).
Khurana, D., Koli, A., Khatter, K., & Singh, S. (2022). Natural language processing: State of the art, current trends and challenges. Multimedia Tools and Applications, 1-32.
Madhu, B., Rahman, M. A., Mukherjee, A., Islam, M. Z., Roy, R., & Ali, L. E. (2021). A comparative study of support vector machine and artificial neural network for option price prediction. Journal of Computer and Communications, 9(5), 78-91.
Özdemir, L., Bilgin, A. (2021). Sağlıkta Yapay zekânın Kullanımı ve Etik Sorunlar. Sağlık ve Hemşirelik Yönetimi Dergisi, 8(3), 439-445.
Rigla, M., García-Sáez, G., Pons, B., Hernando, M. E. (2018). Artificial intelligence methodologies and their application to diabetes. Journal of Diabetes Science and Technology, 12(2), 303-310.
Robert, N. (2019). How artificial intelligence is changing nursing. Nursing management, 50(9), 30.
Shi, Z. Z., & Zheng, N. N. (2006). Progress and challenge of artificial intelligence. Journal of computer science and technology, 21(5), 810-822.
Suthaharan, S. (2014). Big data classification: Problems and challenges in network intrusion prediction with machine learning. ACM SIGMETRICS Performance Evaluation Review, 41(4), 70-73.
Wang, S., Pei, K., Whitehouse, J., Yang, J., & Jana, S. (2018). Efficient formal safety analysis of neural networks. Advances in Neural Information Processing Systems, 31.
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