Araştırma Makalesi
BibTex RIS Kaynak Göster

Sağlık İletişimi ve Yapay Zekâ Kesişimindeki Yayınların Bibliyometrik İncelemesi

Yıl 2024, Sayı: 44, 66 - 90, 28.04.2024
https://doi.org/10.31123/akil.1428134

Öz

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.

Kaynakça

  • 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

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

Yıl 2024, Sayı: 44, 66 - 90, 28.04.2024
https://doi.org/10.31123/akil.1428134

Öz

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

Kaynakça

  • 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
Toplam 92 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular İletişim ve Medya Çalışmaları (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Mesut Ersin Sönmez 0000-0002-0966-9216

Erken Görünüm Tarihi 21 Nisan 2024
Yayımlanma Tarihi 28 Nisan 2024
Gönderilme Tarihi 30 Ocak 2024
Kabul Tarihi 5 Nisan 2024
Yayımlandığı Sayı Yıl 2024 Sayı: 44

Kaynak Göster

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