COVİD 19 SÜRECİNDE YAPAY ZEKÂ VE BİLEŞENLERİ İLE İLGİLİ YAYINLARIN BİBLİYOMETRİK ANALİZİ

Tuğçe Karayel; Mehmet Nurullah Kurutkan

doi:10.52880/sagakaderg.1070774

Araştırma Makalesi

BIBLIOMETRIC ANALYSIS OF PUBLICATIONS RELATED TO ARTIFICIAL INTELLIGENCE AND ITS COMPONENTS IN THE COVID-19 PERIOD

Yıl 2022, Cilt: 9 Sayı: 3, 220 - 233, 01.09.2022

Tuğçe Karayel Mehmet Nurullah Kurutkan

https://doi.org/10.52880/sagakaderg.1070774

Öz

Purpose: The main purpose of this study is to conduct a bibliometric analysis of publications in the field of Covid-19 and artificial intelligence. The performance of the field, its conceptual and social structure, the thematic development map and the identification of its main clusters serve this purpose.
Method: In this article, R-based Bibliometrix, VOSviwer, SciMAT and Citespace software were used. Web of Science articles dec dec 2020-2021 have been downloaded as raw data from the Core collection with the search staretit. In total, 1367 articles were studied. Conceptual and social structure analyses were carried out from information structures with performance analyses. The process has been completed with the analysis of engine themes and main clusters.
Finding: From the point of view of conceptual structure analyses, it was determined that the studies were analyzed under the headings classification, diagnosis and treatment. According to the results of the social structure, the USA, China, India, Italy and the UK are both the most broadcasting countries and the countries that are most open to dec-country cooperation. According to thematic diagram analysis, themes based on artificial intelligence tools and algorithms used in “Transfer- Learning and Support Vector Machines”, covid 19 disease diagnosis, social media, mental health and covid process have come to the fore.
Result: The results of the bibliometric analysis provided information about the quality of published studies on COVID-19 and artificial intelligence, as well as research areas. In particular, artificial intelligence applications based on “Transfer- Learning” and “Support Vector Machines”, forecasting and social media data have the potential to become popular research topics.

Anahtar Kelimeler

Covid-19, Artificial İntelligence, Machine Learning, Artificial Neural Networks Deep Learning

Kaynakça

1. Ai, T., Yang, Z., Hou, H., Zhan, C., Chen, C., & Lv, W., et al. (2020). Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases. Radiology, 296(2).
2. Albahri, O. S., Zaidan, A. A., Albahri, A. S., Zaidan, B. B., Abdulkareem, K. H., Al-qaysi, Z. T., & et al. (2020). Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects. Journal of Infection and Public Health, 13(10), 1381–1396.
3. Ali Abbasian Ardakani, Alireza Rajabzadeh Kanafi, U. Rajendra Acharya, Nazanin Khadem, Afshin Mohammadi, Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks, Computers in Biology and Medicine, Volume 121, 2020.
4. Beck, B. R., Shin, B., Choi, Y., Park, S., & Kang, K. (2020). Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model. Computational and structural biotechnology journal, 18, 784-790.
5. Börner, K., Chen, C., & Boyack, K. W. (2003). Visualizing knowledge domains. Annual review of information science and technology, 37(1), 179-255.
6. Chen, C. (2014). The citespace manual. College of Computing and Informatics, 1, 1-84.
7. Chen, C. (2017). Science mapping: a systematic review of the literature. Journal of data and information science, 2(2).
8. Cobo, M. J., A. G. López-Herrera, E. Herrera-Viedma, and F. Herrera. 2011a. “An Approach for Detecting, Quantifying, and Visualizing the Evolution of a Research Field: A Practical Application to the Fuzzy Sets Theory Field.” Journal of Informetrics 5 (1): 146–166.
9. Cobo, M. J., A. G. López-Herrera, E. Herrera-Viedma, and F. Herrera. 2011b. “Science Mapping Software Tools: Review, Analysis, and Cooperative Study among Tools.” Journal of the American Society for Information Science and Technology 62 (7): 1382–1402.
10. Cobo, M. J., A. G. López-Herrera, E. Herrera-Viedma, and F. Herrera. 2012. “SciMAT: ‘A New Science Mapping Analysis Software Tool’.” Journal of the American Society for Information Science and Technology 63 (8): 1609–1630.
11. Cobo, M. J., Martínez, M.-Á., Gutiérrez-Salcedo, M., Fujita, H., & Herrera-Viedma, E. (2015). 25 years at knowledge-based systems: a bibliometric analysis. Knowledge-Based Systems, 80, 3–13.
12. Cuccurullo, C., Aria, M., & Sarto, F. (2016). Foundations and trends in performance management. A twenty-five years bibliometric analysis in business and public administration domains. Scientometrics, 108(2), 595-611.
13. Deng, J., Hou, X., Zhang, T., Bai, G., Hao, E., Chu, J. J. H., & et al. (2020). Carry forward advantages of traditional medicines in prevention and control of outbreak of COVID-19 pandemic. Chinese Herbal Medicines, 12(3), 207–213. https://doi.org/10.1016/j.chmed.2020.05.003.
14. Gülhan, P. Y., & Kurutkan, M. N. (2021). Bibliometric Analysis of The Last 40 Years of Chest Journal. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 9(4), 1507-1518.
15. Grant, M. J., & Booth, A. (2009). A typology of reviews: an analysis of 14 review types and associated methodologies. Health information & libraries journal, 26(2), 91-108.
16. Hooijdonk, R. V. 2019. What’s behind the AI craze – Just a fad or the ‘real deal’? Accessed January 23, 2021.
17. Jiang, X., Coffee, M., Bari, A., Wang, J., Jiang, X., Huang, J., Shi, J., Dai, J., Cai, J., Zhang, T., Wu, Z., He, G., & Huang, Y. (2020). Towards an artificial intelligence framework for data-driven prediction of coronavirus clinical severity. Computers, Materials and Continua, 63(1), 537–551.
18. Khan, M., Mehran, M. T., Haq, Z. U., Ullah, Z., Naqvi, S. R., Ihsan, M., & Abbass, H. (2021). Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review. Expert Systems with Applications, 185.
19. Kurutkan, M. N., & Orhan, F. (2018). Sağlık politikası konusunun bilim haritalama teknikleri ile analizi. İksad Yayınevi, Türkiye.
20. Li D, Wang D, Dong J, Wang N, Huang H, Xu H, Xia C. False-Negative Results of Real-Time Reverse-Transcriptase Polymerase Chain Reaction for Severe Acute Respiratory Syndrome Coronavirus 2: Role of Deep-Learning-Based CT Diagnosis and Insights from Two Cases. Korean J Radiol. 2020 Apr;21.
21. Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, Bai J, Lu Y, Fang Z, Song Q, Cao K, Liu D, Wang G, Xu Q, Fang X, Zhang S, Xia J, Xia J. Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy. Radiology. 2020 Aug;296(2).
22. Lin, J., Huang, W., Wen, M., Li, D., Ma, S., Hua, J., & et al. (2020). Containing the spread of coronavirus disease 2019 (COVID-19): Meteorological factors and control strategies. Science of the Total Environment, 744(December 2019).
23. Martínez, M. A., Cobo, M. J., Herrera, M., & Herrera-Viedma, E. (2015). Analyzing the scientific evolution of social work using science mapping. Research on Social Work Practice, 25(2), 257–277.
24. Mei, X., Lee, H. C., Diao, K. Y., Huang, M., Lin, B., Liu, C., ... & Yang, Y. (2020). Artificial intelligence–enabled rapid diagnosis of patients with COVID-19. Nature medicine, 26(8), 1224-1228.
25. Murgado-Armenteros, E. & Gutiérrez-Salcedo, María & Ruiz, Francisco José & Cobo, Manuel. (2015). Analysing the conceptual evolution of qualitative marketing research through science mapping analysis. Scientometrics. 102. 10.1007/s11192-014-1443-z.
26. Peters, H., & Van Raan, A. (1991). Structuring scientific activities by co-author analysis: An expercise on a university faculty level. Scientometrics, 20(1), 235-255.
27. Pham, Q. V., Nguyen, D. C., Huynh-The, T., Hwang, W. J., & Pathirana, P. N. (2020). Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts. IEEE Access, 8(April).
28. Tahamtan, A., & Ardebili, A. (2020). Real-time RT-PCR in COVID-19 detection: Issues affecting the results. Expert Review of Molecular Diagnostics, 20(5), 453–454. https:// doi.org/10.1080/14737159.2020.1757437.
29. Vaishya, R., Javaid, M., Khan, I. H., & Haleem, A. (2020). Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes and Metabolic Syndrome: Clinical Research and Reviews, 14(4), 337–339. https://doi.org/10.1016/j.dsx.2020.04.012.
30. Zhavoronkov, A., Aladinskiy, V., Zhebrak, A., Zagribelnyy, B., Terentiev, V., Bezrukov, D.S., Polykovskiy, D., Shayakhmetov, R., Filimonov, A., Orekhov, P., Yan, Y., Popova, O., Vanhaelen, Q., Aliper, A., & Ivanenkov, Y. (2020). Potential 2019-nCoV 3C-like protease inhibitors designed using generative deep learning approaches. Insilico Med Hong Kong Ltd A, 307(2).
31. Zhavoronkov, A., Ivanenkov, Y.A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V., Terentiev, V. A., Polykovskiy, D. A., Kuznetsov, M. D., Asadulaev, A., Volkov, Y., Zholus, A., Shayakhmetov, R.R., Zhebrak, A., Minaeva, L. I., Zagribelnyy, B. A., Lee, L. H., Soll, R., Madge, D., … Aspuru-Guzik, A. (2019). Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nature Biotechnology, 37(9), 1038–1040.
32. Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M., Guo, J., Cai, M., Yang, J., Li, Y., Meng, X., & Xu, B. (2021). A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19). European radiology, 31(8), 6096–6104. https://doi.org/10.1007/s00330-021-07715-1
33. Wang, S., Zha, y., Li, W., Q Wu, Q., Li, X.,, Niu, M., Wang, M., Qiu, X., Li, H., He Yu, Gong, W., Bai, Y.,, Li, L., Zhu, Y., Wang, L., Tian, J., A Fully Automatic Deep Learning System for COVID-19 Diagnostic and Prognostic Analysis European Respiratory Journal Jan 2020, 2000775; DOI: 10.1183/13993003.00775-2020
34. Xu, X., Jiang, X., Ma, C., Du, P., Li. X, Lv, S., Yu, L., Ni, Q., Chen, Y., Su, J., Lang, G., Li, Y., Zhao, H., Liu, J., Xu, K., Ruan, L., Sheng, J., Qiu, Y., Wu, W., Liang, T., Li, L., A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia, Engineering, Volume 6, Issue 10, 2020, Pages 1122-1129, ISSN 2095-8099.
35. Small, H. (1997). Update on science mapping: Creating large document spaces. Scientometrics, 38(2), 275–293.

COVİD 19 SÜRECİNDE YAPAY ZEKÂ VE BİLEŞENLERİ İLE İLGİLİ YAYINLARIN BİBLİYOMETRİK ANALİZİ

Yıl 2022, Cilt: 9 Sayı: 3, 220 - 233, 01.09.2022

Tuğçe Karayel Mehmet Nurullah Kurutkan

https://doi.org/10.52880/sagakaderg.1070774

Öz

Amaç: Bu çalışmanın temel amacı, Covid-19 ve yapay zekâ alanındaki yayınların bibliyometrik analizini yapmaktır. Alanın performansı, kavramsal ve sosyal yapısı, tematik gelişim haritası ve ana kümelerinin tespiti bu amaç altında ortaya çıkarılmıştır.
Yöntem: Bu çalışmada, R tabanlı Bibliometrix, VOSviwer, SciMAT ve Citespace yazılımları kullanılmıştır. Arama stratejisi ile 2020-2021 yılları arasındaki Web of Science makaleleri Core koleksiyonundan ham veri olarak indirilmiştir. Toplamda 1367 makale incelenmiştir. Performans analizleri ile bilgi yapılarından kavramsal ve sosyal yapı analizleri gerçekleştirilmiştir. Motor temalar ile ana kümeler analizi ile süreç tamamlanmıştır.
Bulgu: Kavramsal yapı analizleri açısından bakıldığında çalışmaların sınıflandırma, teşhis ve tedavi başlıkları altında analiz edildiği tespit edildi. Sosyal yapı sonuçlarına göre ise ABD, Çin, Hindistan, İtalya ve İngiltere hem en çok yayın yapan ülkelerdir hem de en çok ülkeler arası iş birliğine açık olan ülkelerdir. Tematik diyagram analizlerine göre “Transfer- Learning ve Support Vector Machines”, covid-19 hastalık teşhisi, sosyal medya, zihin sağlığı ve covid sürecinde kullanılan yapay zekâ araç ve algoritmalarına dayalı temalar ön plana çıkmıştır.
Sonuç: Bibliyometrik analiz sonuçları, COVID-19 ve yapay zekâ ile ilgili yayınlanmış çalışmaların kalitesi ve araştırma alanları hakkında bilgi verdi. Özellikle “Transfer- Learning” ile “Support Vector Machines”, forecasting ve sosyal medya verilerine dayalı yapay zekâ uygulamaları popüler araştırma konuları olma potansiyeli taşımaktadır.

Anahtar Kelimeler

Covid-19, Yapay Zekâ, Makine Öğrenmesi, Yapay Sinir Ağları, Derin Öğrenme

Kaynakça

1. Ai, T., Yang, Z., Hou, H., Zhan, C., Chen, C., & Lv, W., et al. (2020). Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases. Radiology, 296(2).
2. Albahri, O. S., Zaidan, A. A., Albahri, A. S., Zaidan, B. B., Abdulkareem, K. H., Al-qaysi, Z. T., & et al. (2020). Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects. Journal of Infection and Public Health, 13(10), 1381–1396.
3. Ali Abbasian Ardakani, Alireza Rajabzadeh Kanafi, U. Rajendra Acharya, Nazanin Khadem, Afshin Mohammadi, Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks, Computers in Biology and Medicine, Volume 121, 2020.
4. Beck, B. R., Shin, B., Choi, Y., Park, S., & Kang, K. (2020). Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model. Computational and structural biotechnology journal, 18, 784-790.
5. Börner, K., Chen, C., & Boyack, K. W. (2003). Visualizing knowledge domains. Annual review of information science and technology, 37(1), 179-255.
6. Chen, C. (2014). The citespace manual. College of Computing and Informatics, 1, 1-84.
7. Chen, C. (2017). Science mapping: a systematic review of the literature. Journal of data and information science, 2(2).
8. Cobo, M. J., A. G. López-Herrera, E. Herrera-Viedma, and F. Herrera. 2011a. “An Approach for Detecting, Quantifying, and Visualizing the Evolution of a Research Field: A Practical Application to the Fuzzy Sets Theory Field.” Journal of Informetrics 5 (1): 146–166.
9. Cobo, M. J., A. G. López-Herrera, E. Herrera-Viedma, and F. Herrera. 2011b. “Science Mapping Software Tools: Review, Analysis, and Cooperative Study among Tools.” Journal of the American Society for Information Science and Technology 62 (7): 1382–1402.
10. Cobo, M. J., A. G. López-Herrera, E. Herrera-Viedma, and F. Herrera. 2012. “SciMAT: ‘A New Science Mapping Analysis Software Tool’.” Journal of the American Society for Information Science and Technology 63 (8): 1609–1630.
11. Cobo, M. J., Martínez, M.-Á., Gutiérrez-Salcedo, M., Fujita, H., & Herrera-Viedma, E. (2015). 25 years at knowledge-based systems: a bibliometric analysis. Knowledge-Based Systems, 80, 3–13.
12. Cuccurullo, C., Aria, M., & Sarto, F. (2016). Foundations and trends in performance management. A twenty-five years bibliometric analysis in business and public administration domains. Scientometrics, 108(2), 595-611.
13. Deng, J., Hou, X., Zhang, T., Bai, G., Hao, E., Chu, J. J. H., & et al. (2020). Carry forward advantages of traditional medicines in prevention and control of outbreak of COVID-19 pandemic. Chinese Herbal Medicines, 12(3), 207–213. https://doi.org/10.1016/j.chmed.2020.05.003.
14. Gülhan, P. Y., & Kurutkan, M. N. (2021). Bibliometric Analysis of The Last 40 Years of Chest Journal. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 9(4), 1507-1518.
15. Grant, M. J., & Booth, A. (2009). A typology of reviews: an analysis of 14 review types and associated methodologies. Health information & libraries journal, 26(2), 91-108.
16. Hooijdonk, R. V. 2019. What’s behind the AI craze – Just a fad or the ‘real deal’? Accessed January 23, 2021.
17. Jiang, X., Coffee, M., Bari, A., Wang, J., Jiang, X., Huang, J., Shi, J., Dai, J., Cai, J., Zhang, T., Wu, Z., He, G., & Huang, Y. (2020). Towards an artificial intelligence framework for data-driven prediction of coronavirus clinical severity. Computers, Materials and Continua, 63(1), 537–551.
18. Khan, M., Mehran, M. T., Haq, Z. U., Ullah, Z., Naqvi, S. R., Ihsan, M., & Abbass, H. (2021). Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review. Expert Systems with Applications, 185.
19. Kurutkan, M. N., & Orhan, F. (2018). Sağlık politikası konusunun bilim haritalama teknikleri ile analizi. İksad Yayınevi, Türkiye.
20. Li D, Wang D, Dong J, Wang N, Huang H, Xu H, Xia C. False-Negative Results of Real-Time Reverse-Transcriptase Polymerase Chain Reaction for Severe Acute Respiratory Syndrome Coronavirus 2: Role of Deep-Learning-Based CT Diagnosis and Insights from Two Cases. Korean J Radiol. 2020 Apr;21.
21. Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, Bai J, Lu Y, Fang Z, Song Q, Cao K, Liu D, Wang G, Xu Q, Fang X, Zhang S, Xia J, Xia J. Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy. Radiology. 2020 Aug;296(2).
22. Lin, J., Huang, W., Wen, M., Li, D., Ma, S., Hua, J., & et al. (2020). Containing the spread of coronavirus disease 2019 (COVID-19): Meteorological factors and control strategies. Science of the Total Environment, 744(December 2019).
23. Martínez, M. A., Cobo, M. J., Herrera, M., & Herrera-Viedma, E. (2015). Analyzing the scientific evolution of social work using science mapping. Research on Social Work Practice, 25(2), 257–277.
24. Mei, X., Lee, H. C., Diao, K. Y., Huang, M., Lin, B., Liu, C., ... & Yang, Y. (2020). Artificial intelligence–enabled rapid diagnosis of patients with COVID-19. Nature medicine, 26(8), 1224-1228.
25. Murgado-Armenteros, E. & Gutiérrez-Salcedo, María & Ruiz, Francisco José & Cobo, Manuel. (2015). Analysing the conceptual evolution of qualitative marketing research through science mapping analysis. Scientometrics. 102. 10.1007/s11192-014-1443-z.
26. Peters, H., & Van Raan, A. (1991). Structuring scientific activities by co-author analysis: An expercise on a university faculty level. Scientometrics, 20(1), 235-255.
27. Pham, Q. V., Nguyen, D. C., Huynh-The, T., Hwang, W. J., & Pathirana, P. N. (2020). Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts. IEEE Access, 8(April).
28. Tahamtan, A., & Ardebili, A. (2020). Real-time RT-PCR in COVID-19 detection: Issues affecting the results. Expert Review of Molecular Diagnostics, 20(5), 453–454. https:// doi.org/10.1080/14737159.2020.1757437.
29. Vaishya, R., Javaid, M., Khan, I. H., & Haleem, A. (2020). Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes and Metabolic Syndrome: Clinical Research and Reviews, 14(4), 337–339. https://doi.org/10.1016/j.dsx.2020.04.012.
30. Zhavoronkov, A., Aladinskiy, V., Zhebrak, A., Zagribelnyy, B., Terentiev, V., Bezrukov, D.S., Polykovskiy, D., Shayakhmetov, R., Filimonov, A., Orekhov, P., Yan, Y., Popova, O., Vanhaelen, Q., Aliper, A., & Ivanenkov, Y. (2020). Potential 2019-nCoV 3C-like protease inhibitors designed using generative deep learning approaches. Insilico Med Hong Kong Ltd A, 307(2).
31. Zhavoronkov, A., Ivanenkov, Y.A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V., Terentiev, V. A., Polykovskiy, D. A., Kuznetsov, M. D., Asadulaev, A., Volkov, Y., Zholus, A., Shayakhmetov, R.R., Zhebrak, A., Minaeva, L. I., Zagribelnyy, B. A., Lee, L. H., Soll, R., Madge, D., … Aspuru-Guzik, A. (2019). Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nature Biotechnology, 37(9), 1038–1040.
32. Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M., Guo, J., Cai, M., Yang, J., Li, Y., Meng, X., & Xu, B. (2021). A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19). European radiology, 31(8), 6096–6104. https://doi.org/10.1007/s00330-021-07715-1
33. Wang, S., Zha, y., Li, W., Q Wu, Q., Li, X.,, Niu, M., Wang, M., Qiu, X., Li, H., He Yu, Gong, W., Bai, Y.,, Li, L., Zhu, Y., Wang, L., Tian, J., A Fully Automatic Deep Learning System for COVID-19 Diagnostic and Prognostic Analysis European Respiratory Journal Jan 2020, 2000775; DOI: 10.1183/13993003.00775-2020
34. Xu, X., Jiang, X., Ma, C., Du, P., Li. X, Lv, S., Yu, L., Ni, Q., Chen, Y., Su, J., Lang, G., Li, Y., Zhao, H., Liu, J., Xu, K., Ruan, L., Sheng, J., Qiu, Y., Wu, W., Liang, T., Li, L., A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia, Engineering, Volume 6, Issue 10, 2020, Pages 1122-1129, ISSN 2095-8099.
35. Small, H. (1997). Update on science mapping: Creating large document spaces. Scientometrics, 38(2), 275–293.

Toplam 35 adet kaynakça vardır.

Ayrıntılar

Birincil Dil	Türkçe
Konular	Sağlık Kurumları Yönetimi
Bölüm	Araştırma
Yazarlar	Tuğçe Karayel 0000-0002-5556-225X Mehmet Nurullah Kurutkan 0000-0002-3740-4231
Yayımlanma Tarihi	1 Eylül 2022
Kabul Tarihi	23 Mart 2022
Yayımlandığı Sayı	Yıl 2022 Cilt: 9 Sayı: 3

Kaynak Göster

APA	Karayel, T., & Kurutkan, M. N. (2022). COVİD 19 SÜRECİNDE YAPAY ZEKÂ VE BİLEŞENLERİ İLE İLGİLİ YAYINLARIN BİBLİYOMETRİK ANALİZİ. Sağlık Akademisyenleri Dergisi, 9(3), 220-233. https://doi.org/10.52880/sagakaderg.1070774

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