Sağlık İletişimi ve Yapay Zekâ Kesişimindeki Yayınların Bibliyometrik İncelemesi

Mesut Ersin Sönmez

doi:10.31123/akil.1428134

EN TR

Bibliometric Analysis of Publications at the Intersection of Health Communication and Artificial Intelligence

Abstract

The pandemic has globally affected every aspect and has brought painful experiences to humanity. During and after the pandemic period, covering the years 2019-2023, determining the impacts of artificial intelligence (AI) technologies on health communication is of critical importance for accurate information dissemination and improvement of health services. This study has examined how AI technologies are utilized in health communication and the transformations they have brought in areas such as health services, disease surveillance, epidemic monitoring, and patient education materials. It discusses how AI techniques are effective in analyzing health data, medical imaging, and the dissemination of health information. The bibliometric analysis conducted deeply investigates the works done in health communication and artificial intelligence, aiming to illuminate the characteristics and development processes of these fields. The quantitative distribution and impact levels of publications in the literature have been evaluated, highlighting the historical and current trends of the research area. In the conclusion, it is noted that AI has undergone significant evolution in the field of health communication, and the ongoing development of these technologies will lead to innovation and progress in health. These technological advancements are emphasized for their potential to enhance the quality of health services, provide health information to the public, and support healthy decision-making processes

Keywords

Health Communication, Artificial Intelligence, Machine Learning, Web of Science, Bibliometric Analysis

Sağlık İletişimi ve Yapay Zekâ Kesişimindeki Yayınların Bibliyometrik İncelemesi

Abstract

Pandemi küresel anlamda her alanı etkilemiş ve insanlık için acı tecrübeler yaşatmıştır. Pandemi dönemi ve sonrasını kapsayan 2019-2023 yılları arasında, yapay zekâ (AI) teknolojilerinin sağlık iletişimine olan etkilerinin belirlenmesi doğru bilgilendirme ve sağlık hizmetlerinin iyileştirilmesi açısından kritik önem taşımaktadır. AI teknolojilerinin sağlık iletişiminde nasıl kullanıldığı ve bu kullanımın sağlık hizmetleri, hastalık gözetimi, salgın izleme ve hasta eğitim materyalleri gibi alanlarda yarattığı dönüşümler incelenmiştir. Bu çalışmada, AI tekniklerinin sağlık verilerinin analizi, tıbbi görüntüleme ve sağlık bilgisinin yayılmasında nasıl etkili olduğunu tartışılmıştır. Yapılan bibliyometrik analiz, sağlık iletişimi ve yapay zekâ konularında yapılan çalışmaları derinlemesine incelenerek, bu alanların karakteristiklerini ve gelişim süreçlerini aydınlatılmaya çalışılmıştır. Literatürdeki yayınların niceliksel dağılımı ve etki düzeyleri değerlendirilerek, araştırma alanının tarihsel ve güncel eğilimleri ortaya konulmuştur. Sonuç bölümünde, Yapay zekânın sağlık iletişimi alanında önemli bir evrim geçirdiği ve bu teknolojilerin devam eden gelişiminin sağlık alanında yenilik ve ilerlemeye yol açacağı belirtilmiştir. Bu teknolojik ilerlemelerin sağlık hizmetlerinin kalitesini artırma, halka sağlık bilgisi sunma ve sağlıklı karar alma süreçlerini destekleme potansiyeline sahip olduğu vurgulanmıştır.

Keywords

Sağlık İletişimi, Yapay Zekâ, Makine Öğrenmesi, Web of Science, Bibliyometrik Analiz

References

Aftab, M. O., Rehman, A. U., Farooq, M. S., & Vistro, D. M. (2021). Predicting Growth and Trends of COVID-19 by Implementing Machine Learning Algorithms Proceedings of the 3rd International Conference on Integrated Intelligent Computing Communication & Security (ICIIC 2021),
Ahadi, A., Singh, A., Bower, M., & Garrett, M. (2022). Text Mining in Education—A Bibliometrics-Based Systematic Review. Education Sciences, 12(3). https://doi.org/10.3390/educsci12030210
Ali, H., Mahadevamurthy, M., & Jagadeesha, B. M. (2015). A bibliometric analysis of the Journal of Academic Librarianship. International Journal of Library and Information Studies, 5(4), 83-90.
Aria, M., & Cuccurullo, C. (2017). bibliometrix : An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007
Atkin, C., & Silk, K. (2014). Health Communication. In D. W. Stacks & M. B. Salwen (Eds.), An Integrated Approach to Communication Theory and Research (2 ed., pp. 503-517). Routledge. https://doi.org/10.4324/9780203887011-40
Avcı, K., & Avşar, Z. (2014). Sağlık İletişimi ve Yeni Medya. İletişim Kuram ve Araştırma Dergisi(39), 181-190.
Aydoğan, H. (2023). Dijital Ebeveynlik ve Reklamcılık Odağındaki Araştırmaların Bibliyometrik İncelemesi: Mevcut Eğilimler ve Gelecek Yönelimleri. TRT Akademi, 08(19), 876-903. https://doi.org/10.37679/trta.1328217
Ball, R. (2018). An Introduction to Bibliometrics: New Development and Trends. Chandos Publishing.
Benjamin, E. J., Muntner, P., Alonso, A., Bittencourt, M. S., Callaway, C. W., Carson, A. P., Chamberlain, A. M., Chang, A. R., Cheng, S., Das, S. R., Delling, F. N., Djousse, L., Elkind, M. S. V., Ferguson, J. F., Fornage, M., Jordan, L. C., Khan, S. S., Kissela, B. M., Knutson, K. L., . . . Stroke Statistics, S. (2019). Heart Disease and Stroke Statistics-2019 Update: A Report From the American Heart Association. Circulation, 139(10), e56-e528. https://doi.org/10.1161/CIR.0000000000000659
Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32. https://doi.org/10.1023/a:1010933404324

Cabatuan, M., & Manguerra, M. (2020). Machine learning for disease surveillance or outbreak monitoring: A review 2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM),
Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). 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. https://doi.org/10.1002/asi.21525
Corman, V. M., Landt, O., Kaiser, M., Molenkamp, R., Meijer, A., Chu, D. K., Bleicker, T., Brünink, S., Schneider, J., Schmidt, M. L., Mulders, D. G., Haagmans, B. L., van der Veer, B., van den Brink, S., Wijsman, L., Goderski, G., Romette, J.-L., Ellis, J., Zambon, M., . . . Drosten, C. (2020). Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR. EuroSurveillance, 25(3), Article 2000045. https://doi.org/https://doi.org/10.2807%2F1560-7917.ES.2020.25.3.2000045
Coronaviridae Study Group of the International Committee on Taxonomy of, V. (2020). The species Severe acute respiratory syndrome-related coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2. Nat Microbiol, 5(4), 536-544. https://doi.org/10.1038/s41564-020-0695-z
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297. https://doi.org/10.1007/bf00994018
Covello, V. T. (2016). Risk Communication: An Emerging Area of Health Communication Research. Annals of the International Communication Association, 15(1), 359-373. https://doi.org/10.1080/23808985.1992.11678816
Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21-27. https://doi.org/10.1109/tit.1967.1053964
Dang, Q., Luo, Z., Ouyang, C., & Wang, L. (2021). First Systematic Review on Health Communication Using the CiteSpace Software in China: Exploring Its Research Hotspots and Frontiers. Int J Environ Res Public Health, 18(24). https://doi.org/10.3390/ijerph182413008
Deng, J., Dong, W., Socher, R., Li, L.-J., Kai, L., & Li, F.-F. (2009). ImageNet: A large-scale hierarchical image database 2009 IEEE Conference on Computer Vision and Pattern Recognition,
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285-296. https://doi.org/10.1016/j.jbusres.2021.04.070
Donthu, N., Reinartz, W., Kumar, S., & Pattnaik, D. (2021). A retrospective review of the first 35 years of the International Journal of Research in Marketing. International Journal of Research in Marketing, 38(1), 232-269. https://doi.org/10.1016/j.ijresmar.2020.10.006
Efendi, T., Lubis, F. F., Mutaqin, P., A., Waskita, D., Sulistyaningtyas, T., Rosmansyah, Y., & Sembiring, J. (2022). A Bibliometrics-Based Systematic Review on Automated Essay Scoring in Education. 2022 International Conference on Information Technology Systems and Innovation (ICITSI),
Ellegaard, O., & Wallin, J. A. (2015). The bibliometric analysis of scholarly production: How great is the impact? Scientometrics, 105(3), 1809-1831. https://doi.org/10.1007/s11192-015-1645-z
Emmons, S., Kobourov, S., Gallant, M., & Borner, K. (2016). Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale. PLoS One, 11(7), e0159161. https://doi.org/10.1371/journal.pone.0159161
Evans, R., & Brown, H. (2019). Artificial intelligence for health communication: Prospects and challenges. AI & Society, 34(4), 855-866.
Franco, P., De Felice, F., Jagsi, R., Nader Marta, G., Kaidar-Person, O., Gabrys, D., Kim, K., Ramiah, D., Meattini, I., & Poortmans, P. (2023). Breast cancer radiation therapy: A bibliometric analysis of the scientific literature. Clin Transl Radiat Oncol, 39, 100556. https://doi.org/10.1016/j.ctro.2022.11.015
Freimuth, V. S., & Quinn, S. C. (2004). The contributions of health communication to eliminating health disparities. Am J Public Health, 94(12), 2053-2055. https://doi.org/10.2105/ajph.94.12.2053
Ghiasee, A. (2022). A holistic view on health communication during the Covid-19 pandemic: An analysis with science mapping technique. J Soc Anal Health, 2(2), 125-141. https://doi.org/10.5281/zenodo.6769963
Ghimire, A., Thapa, S., Jha, A. K., Kumar, A., Kumar, A., & Adhikari, S. (2020). AI and IoT Solutions for Tackling COVID-19 Pandemic 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA),
Goodfellow, I., Courville, A., & Bengio, Y. (2016). Deep Learning: Adaptive Computation and Machine Learning series. The MIT Press.
Guan, W. J., Ni, Z. Y., Hu, Y., Liang, W. H., Ou, C. Q., He, J. X., Liu, L., Shan, H., Lei, C. L., Hui, D. S. C., Du, B., Li, L. J., Zeng, G., Yuen, K. Y., Chen, R. C., Tang, C. L., Wang, T., Chen, P. Y., Xiang, J., . . . China Medical Treatment Expert Group for, C. (2020). Clinical Characteristics of Coronavirus Disease 2019 in China. N Engl J Med, 382(18), 1708-1720. https://doi.org/10.1056/NEJMoa2002032
Guo, Y., Hao, Z., Zhao, S., Gong, J., & Yang, F. (2020). Artificial Intelligence in Health Care: Bibliometric Analysis. J Med Internet Res, 22(7), e18228. https://doi.org/10.2196/18228
He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2020). Mask R-CNN. IEEE Trans Pattern Anal Mach Intell, 42(2), 386-397. https://doi.org/10.1109/TPAMI.2018.2844175
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
Hesse, B. W., Nelson, D. E., Kreps, G. L., Croyle, R. T., Arora, N. K., Rimer, B. K., & Viswanath, K. (2005). Trust and sources of health information: the impact of the Internet and its implications for health care providers: findings from the first Health Information National Trends Survey. Arch Intern Med, 165(22), 2618-2624. https://doi.org/10.1001/archinte.165.22.2618
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780. Hoffmann, M., Kleine-Weber, H., Schroeder, S., Kruger, N., Herrler, T., Erichsen, S., Schiergens, T. S., Herrler, G., Wu, N. H., Nitsche, A., Muller, M. A., Drosten, C., & Pohlmann, S. (2020). SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor. Cell, 181(2), 271-280 e278. https://doi.org/10.1016/j.cell.2020.02.052
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., Cheng, Z., Yu, T., Xia, J., Wei, Y., Wu, W., Xie, X., Yin, W., Li, H., Liu, M., . . . Cao, B. (2020). Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet, 395(10223), 497-506. https://doi.org/10.1016/S0140-6736(20)30183-5
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely Connected Convolutional Networks 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)Ishikawa, H., & Kiuchi, T. (2010). Health literacy and health communication. Biopsychosoc Med, 4, 18. https://doi.org/10.1186/1751-0759-4-18
Jaagrit, Sharma, V., Rani, L., & Srivastava, D. (2023). Prediction of Coronavirus Using Various Machine Learning Algorithms 2023 International Conference on IoT, Communication and Automation Technology (ICICAT),
Johnson, L., & Kumar, S. (2021). Machine learning in public health surveillance: A systematic review. Health Informatics Journal, 27(2), 1460458221991273.
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Zidek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., . . . Hassabis, D. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583-589. https://doi.org/10.1038/s41586-021-03819-2
Kent Baker, H., & Filbeck, G. (2020). Portfolio theory and management. Oxford University Press.
Kickbusch, I., & Maag, D. (2008). Health literacy. In K. Heggenhougen & S. Quah (Eds.), Encyclopedia of public health (pp. 204-211). Academic Press.
Kingma, D. P., & Ba, J. (2015). Adam: A Method for Stochastic Optimization ICLR,
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90. https://doi.org/10.1145/3065386
Kumari, P., Rani, N., & Suresh Kumar, N. (2022). An Ingenious Method to Detect COVID in X-Ray Images Using Machine Learning Techniques 2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N),
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE,
Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
Maron, M. E. (1961). Automatic Indexing: An Experimental Inquiry. Journal of the ACM, 8(3), 404-417. https://doi.org/10.1145/321075.321084
Mendi, B. (2015). Sağlık İletişiminde Sosyal Medyanın Kullanımı: Dünyadaki ve Türkiye'deki Uygulamalar. Öneri Dergisi, 11(44), 275-290.
Mendi, B., & Oğuz, G. (2018). Üniversite Öğrencilerinin İletişim Becerilerinin Sosyal Medyayı Kullanım Özellikleri Bağlamında İncelenmesi: Bir Vakıf Üniversitesinde Sağlık Bilimleri Öğrencileri Üzerinde Değerlendirme. Gümüşhane Üniversitesi İletişim Fakültesi Elektronik Dergisi, 6(1), 666-690. https://doi.org/https://doi.org/10.19145/e-gifder.337976
Mongeon, P., & Paul-Hus, A. (2015). The journal coverage of Web of Science and Scopus: a comparative analysis. Scientometrics, 106(1), 213-228. https://doi.org/10.1007/s11192-015-1765-5
Muhl, D. D., & de Oliveira, L. (2022). A bibliometric and thematic approach to agriculture 4.0. Heliyon, 8(5), e09369. https://doi.org/10.1016/j.heliyon.2022.e09369
Napoli, P. M. (2001). Consumer use of medical information from electronic and paper media. In R. E. Rice & J. E. Katz (Eds.), The internet and health communication: Experiences and expectations (pp. 79-98). SAGE.
Nutbeam, D. (1998). Health promotion glossary. Health Promot Int, 13, 349-364.
Passalacqua, R., Caminiti, C., Salvagni, S., Barni, S., Beretta, G. D., Carlini, P., Contu, A., Di Costanzo, F., Toscano, L., & Campione, F. (2004). Effects of media information on cancer patients' opinions, feelings, decision-making process and physician-patient communication. Cancer, 100(5), 1077-1084. https://doi.org/10.1002/cncr.20050
Patil, T., & Rahman, Z. (2022). A bibliometric analysis of scientific literature on guilt in marketing. Management Review Quarterly, 73(3), 1385-1415. https://doi.org/10.1007/s11301-022-00277-6
Patra, S. K., & Mishra, S. (2013). Bibliometric study of bioinformatics literature. Scientometrics, 67(3), 477-489. https://doi.org/10.1556/Scient.67.2006.3.9
Pritchard, A. (1969). Statistical bibliography or bibliometrics? Journal of Documentation, 25(4), 348-349.
Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81-106. https://doi.org/10.1007/bf00116251
Ramos‐Rodríguez, A. R., & Ruíz‐Navarro, J. (2004). Changes in the intellectual structure of strategic management research: a bibliometric study of the Strategic Management Journal, 1980–2000. Strategic Management Journal, 25(10), 981-1004. https://doi.org/10.1002/smj.397
RDCT. (2014). R: A language and environment for statistical computing. R foundation for statistical computing.
Ren, S., He, K., Girshick, R., & Sun, J. (2017). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Trans Pattern Anal Mach Intell, 39(6), 1137-1149. https://doi.org/10.1109/TPAMI.2016.2577031
Rogers, E. M. (2016). The Field of Health Communication Today. American Behavioral Scientist, 38(2), 208-214. https://doi.org/10.1177/0002764294038002003
Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 (pp. 234-241). https://doi.org/10.1007/978-3-319-24574-4_28
Rusk, N. (2015). Deep learning. Nature Methods, 13(1), 35-35. https://doi.org/10.1038/nmeth.3707 Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., & Fei-Fei, L. (2015). ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision, 115(3), 211-252. https://doi.org/10.1007/s11263-015-0816-y
Sardar, P., Biswas, S., Bhatia, D., & Mukherjee, M. (2023). AI Based approaches for identification of COVID and non-COVID Pneumonia 2023 4th International Conference on Computing and Communication Systems (I3CS),
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization 2017 IEEE International Conference on Computer Vision (ICCV),
Siegel, R. L., Miller, K. D., Fuchs, H. E., & Jemal, A. (2021). Cancer Statistics, 2021. CA Cancer J Clin, 71(1), 7-33. https://doi.org/10.3322/caac.21654
Siegel, R. L., Miller, K. D., Fuchs, H. E., & Jemal, A. (2022). Cancer statistics, 2022. CA Cancer J Clin, 72(1), 7-33. https://doi.org/10.3322/caac.21708
Siegel, R. L., Miller, K. D., & Jemal, A. (2020). Cancer statistics, 2020. CA Cancer J Clin, 70(1), 7-30. https://doi.org/10.3322/caac.21590
Simonyan, K., & Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. . https://doi.org/https://doi.org/10.48550/arXiv.1409.1556
Singh, K., Misra, M., & Yadav, J. (2021). Artificial Intelligence and Machine Learning as a Tool for Combating COVID-19: A Case Study on Health-Tech Start-ups 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT),
Smith, J., & Doe, A. (2020). The integration of artificial intelligence in health communication: A review. Journal of Health Communication, 25(1), 81-92.
Spitzer, R. L., Kroenke, K., Williams, J. B., & Lowe, B. (2006). A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med, 166(10), 1092-1097. https://doi.org/10.1001/archinte.166.10.1092
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A Simple Way to Prevent Neural Networks from Overfitting. JMLR, 15(56), 1929-1958.
Sunori, S. K., Juneja, P., Negi, P. B., Maurya, S., Raj, P., & Nainwal, D. (2021). AI and Machine Learning Based Classification of Air Quality Index Using COVID-19 Lockdown Period Data 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC),
Thompson, D. F., & Walker, C. K. (2015). A descriptive and historical review of bibliometrics with applications to medical sciences. Pharmacotherapy, 35(6), 551-559. https://doi.org/10.1002/phar.1586
Tripathi, V., & Thukral, S. (2018). Determinants of financing of outward foreign direct investment by Indian MNEs. International Journal of Emerging Markets, 13(5), 1154-1181. https://doi.org/10.1108/IJoEM-12-2016-0333
van der Maaten, L., & Hinton, G. (2008). Visualizing Data using t-SNE. JMLR, 9(86), 2579-2605.
Verma, S., Yadav, S. K., & Raj, R. (2023). Trends in the Evaluation of Masstige Marketing: A Bibliometric Analysis Using R. Vision: The Journal of Business Perspective. https://doi.org/10.1177/09722629231172046
Viswanath, K. (2008). Health Communication. In The International Encyclopedia of Communication (pp. 1-16). John Wiley & Sons. https://doi.org/10.1002/9781405186407.wbiech009
Wang, C., Pan, R., Wan, X., Tan, Y., Xu, L., Ho, C. S., & Ho, R. C. (2020). Immediate Psychological Responses and Associated Factors during the Initial Stage of the 2019 Coronavirus Disease (COVID-19) Epidemic among the General Population in China. Int J Environ Res Public Health, 17(5), Article 1729. https://doi.org/https://doi.org/10.3390%2Fijerph17051729
Wrapp, D., Wang, N., Corbett, K. S., Goldsmith, J. A., Hsieh, C. L., Abiona, O., Graham, B. S., & McLellan, J. S. (2020). Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation. Science, 367(6483), 1260-1263. https://doi.org/10.1126/science.abb2507
Wright, K. B., Sparks, L., & O'Hair, H. D. (2013). Health Communication in the 21st Century. Wiley-Blackwell.
Wu, F., Zhao, S., Yu, B., Chen, Y. M., Wang, W., Song, Z. G., Hu, Y., Tao, Z. W., Tian, J. H., Pei, Y. Y., Yuan, M. L., Zhang, Y. L., Dai, F. H., Liu, Y., Wang, Q. M., Zheng, J. J., Xu, L., Holmes, E. C., & Zhang, Y. Z. (2020). A new coronavirus associated with human respiratory disease in China. Nature, 579(7798), 265-269. https://doi.org/10.1038/s41586-020-2008-3
Yeşildal, M., Akman Dömbekci, H., & Öztürk, Y. E. (2021). Sağlık İletişimi Sorunları: Bir Ölçek Geliştirme Çalışması. Türkiye Sosyal Hizmet Araştırmaları Dergisi, 5(2), 108-119.
Yıldırım Becerikli, S. (2013). Türkiye’de sağlık iletişimi üzerine yazılan lisansüstü tezlerin bibliyometrik analizi: eleştirel bir bakış. Ankara Sağlık Hizmetleri Dergisi, 25-36. https://doi.org/https://doi.org/10.1501/Ashd_0000000089
Yılmaz, D., & Günay, M. A. (2022). Türkiye'de Sağlık İletişimi: Sağlık Çalışanları Üzerine Yapılmış Bir Araştırma. İnönü Üniversitesi İletişim Fakültesi E-Dergisi, 7(1), 75-91. https://doi.org/https://doi.org/10.47107/inifedergi.977601
Zeng, L. (2023). Changes in health communication in the age of COVID-19: A study on the dissemination of preprints to the public. Front Public Health, 11, 1078115. https://doi.org/10.3389/fpubh.2023.1078115
Zhu, N., Zhang, D., Wang, W., Li, X., Yang, B., Song, J., Zhao, X., Huang, B., Shi, W., Lu, R., Niu, P., Zhan, F., Ma, X., Wang, D., Xu, W., Wu, G., Gao, G. F., Tan, W., China Novel Coronavirus, I., & Research, T. (2020). A Novel Coronavirus from Patients with Pneumonia in China, 2019. N Engl J Med, 382(8), 727-733. https://doi.org/10.1056/NEJMoa2001017

Details

Primary Language

Turkish

Subjects

Communication and Media Studies (Other)

Journal Section

Research Article

Authors

Mesut Ersin Sönmez ^*
0000-0002-0966-9216
Türkiye

Early Pub Date

April 21, 2024

Publication Date

April 28, 2024

Submission Date

January 30, 2024

Acceptance Date

April 5, 2024

Published in Issue

Year 2024 Number: 44

DOI

https://doi.org/10.31123/akil.1428134

IZ

https://izlik.org/JA87NA62MT

APA

Sönmez, M. E. (2024). Sağlık İletişimi ve Yapay Zekâ Kesişimindeki Yayınların Bibliyometrik İncelemesi. Akdeniz Üniversitesi İletişim Fakültesi Dergisi, 44, 66-90. https://doi.org/10.31123/akil.1428134

AMA

1.Sönmez ME. Sağlık İletişimi ve Yapay Zekâ Kesişimindeki Yayınların Bibliyometrik İncelemesi. Journal of Akdeniz University Faculty of Communication - JAUFC. 2024;(44):66-90. doi:10.31123/akil.1428134

Chicago

Sönmez, Mesut Ersin. 2024. “Sağlık İletişimi Ve Yapay Zekâ Kesişimindeki Yayınların Bibliyometrik İncelemesi”. Akdeniz Üniversitesi İletişim Fakültesi Dergisi, nos. 44: 66-90. https://doi.org/10.31123/akil.1428134.

EndNote

Sönmez ME (April 1, 2024) Sağlık İletişimi ve Yapay Zekâ Kesişimindeki Yayınların Bibliyometrik İncelemesi. Akdeniz Üniversitesi İletişim Fakültesi Dergisi 44 66–90.

IEEE

[1]M. E. Sönmez, “Sağlık İletişimi ve Yapay Zekâ Kesişimindeki Yayınların Bibliyometrik İncelemesi”, Journal of Akdeniz University Faculty of Communication - JAUFC, no. 44, pp. 66–90, Apr. 2024, doi: 10.31123/akil.1428134.

ISNAD

Sönmez, Mesut Ersin. “Sağlık İletişimi Ve Yapay Zekâ Kesişimindeki Yayınların Bibliyometrik İncelemesi”. Akdeniz Üniversitesi İletişim Fakültesi Dergisi. 44 (April 1, 2024): 66-90. https://doi.org/10.31123/akil.1428134.

JAMA

1.Sönmez ME. Sağlık İletişimi ve Yapay Zekâ Kesişimindeki Yayınların Bibliyometrik İncelemesi. Journal of Akdeniz University Faculty of Communication - JAUFC. 2024;:66–90.

MLA

Sönmez, Mesut Ersin. “Sağlık İletişimi Ve Yapay Zekâ Kesişimindeki Yayınların Bibliyometrik İncelemesi”. Akdeniz Üniversitesi İletişim Fakültesi Dergisi, no. 44, Apr. 2024, pp. 66-90, doi:10.31123/akil.1428134.

Vancouver

1.Mesut Ersin Sönmez. Sağlık İletişimi ve Yapay Zekâ Kesişimindeki Yayınların Bibliyometrik İncelemesi. Journal of Akdeniz University Faculty of Communication - JAUFC. 2024 Apr. 1;(44):66-90. doi:10.31123/akil.1428134

33280 33281

Journal of Akdeniz University Faculty of Communication is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC-BY-NC).

Bibliometric Analysis of Publications at the Intersection of Health Communication and Artificial Intelligence

Abstract

Keywords

Sağlık İletişimi ve Yapay Zekâ Kesişimindeki Yayınların Bibliyometrik İncelemesi

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Early Pub Date

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite