Araştırma Makalesi
BibTex RIS Kaynak Göster

Health Communication in the Digital Age: A Bibliometric Analysis of Artificial Intelligence Applications Between 2000-2025

Yıl 2025, Cilt: 16 Sayı: 31, 42 - 70, 31.12.2025
https://izlik.org/JA37SS95KW

Öz

This study presents a bibliometric analysis of academic research on health communication and artificial intelligence published in the Web of Science database between 2000 and 2025. The research examined 367,665 academic publications, analyzing publication types, temporal distribution, author collaboration networks, keyword trends, and geographical distribution patterns. The findings demonstrate that artificial intelligence applications in health communication experienced rapid growth particularly after 2015, reaching peak levels between 2021 and 2024. Publication analysis reveals that 43.64 percent of studies were articles and 40.55 percent were conference proceedings. Keyword frequency analysis identified "artificial intelligence" with 257 occurrences, "machine learning" with 223 occurrences, and "deep learning" with 151 occurrences as the most frequently utilized terms in the literature. The post-pandemic period witnessed the emergence of chatbots, natural language processing, and digital health as prominent research themes. Author collaboration network analysis indicates that Pham Quoc-Viet and Alazab Mamoun occupy central positions within the research community. Geographical analysis demonstrates that the United States, United Kingdom, France, Germany, Sweden, Israel, and Spain represent the leading countries in scientific output within this domain. The results indicate that artificial intelligence and health communication research has evolved into a multidisciplinary structure, reflecting the convergence of technological innovation and healthcare delivery systems. The study emphasizes that while these developments possess significant potential to transform future healthcare services, critical considerations regarding ethics, privacy protection, and algorithmic bias require careful attention and systematic evaluation. The study emphasizes that alongside these developments, which have the potential to shape the future of healthcare services, issues such as ethics, privacy, and algorithmic bias must also be carefully addressed

Kaynakça

  • Adnan, A., Irvine, R. E., Williams, A., Harris, M., & Antonacci, G. (2025). Improving acceptability of mHealth apps-The use of the technology acceptance model to assess the acceptability of mHealth apps: Systematic review. Journal of Medical Internet Research, 27, e66432. https://doi.org/10.2196/66432
  • Agarwal, R., Gao, G., DesRoches, C., & Jha, A. K. (2010). Research commentary-The digital transformation of healthcare: current status and the road ahead. Information Systems Research, 21(4), 796-809. https://doi.org/10.1287/isre.1100.0327
  • Albahri, A. S., Duhaim, A. M., Fadhel, M. A., Alnoor, A., Baqer, N. S., Alzubaidi, L., Albahri, O. S., Alamoodi, A. H., Bai, J., Salhi, A., Santamaría, J., Ouyang, C., Gupta, A., Gu, Y., & Deveci, M. (2023). A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion. Information Fusion, 96, 156-191. https://doi.org/10.1016/j.inffus.2023.03.008
  • Amugongo, L. M., Kriebitz, A., Boch, A., & Lütge, C. (2025). Operationalising AI ethics through the agile software development lifecycle: A case study of AI-enabled mobile health applications. AI and Ethics, 5, 227-244. https://doi.org/10.1007/s43681-023-00331-3
  • Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975.
  • Asan, O., Bayrak, A. E., & Choudhury, A. (2020). Artificial intelligence and human trust in healthcare: Focus on clinicians. Journal of Medical Internet Research, 22(6), e15154. https://doi.org/10.2196/15154
  • Badr, J., Motulsky, A., & Denis, J.-L. (2024). Digital health technologies and inequalities: A scoping review of potential impacts and policy recommendations. Health Policy, 146, 105122. https://doi.org/10.1016/j.healthpol.2024.105122
  • Balcombe, L. (2023). AI chatbots in digital mental health. Informatics, 10(4), 82. https://doi.org/10.3390/informatics10040082
  • Berkman, N. D., Sheridan, S. L., Donahue, K. E., Halpern, D. J., & Crotty, K. (2011). Low health literacy and health outcomes: An updated systematic review. Annals of Internal Medicine, 155(2), 97-107. https://doi.org/10.7326/0003-4819-155-2-201107190-00005
  • Bezuidenhout, N. (2024). Digital communication. Medical Writing, 33(2), 74. https://doi.org/10.56012/gsrj5809
  • Bhardwaj, U., H, M., Gambhir, V., Das, A., Sudhir, R., Kaur, A., & Dev, A. (2024). Analyzing the impact of digital health communication on patient engagement and treatment adherence. Seminars in Medical Writing and Education, 3, 492. https://doi.org/10.56294/mw2024492
  • Bidino, R. D., Daugbjerg, S., Papavero, S. C., Haraldsen, I. H., Cicchetti, A., & Sacchini, D. (2024). Health technology assessment framework for artificial intelligence-based technologies. International Journal of Technology Assessment in Health Care, 40(1), e61. https://doi.org/10.1017/S0266462324000308
  • Borg, K., Boulet, M., Smith, L., & Bragge, P. (2018). Digital inclusion & health communication: A rapid review of literature. Health Communication, 33(10), 1237-1249. https://doi.org/10.1080/10410236.2018.1485077
  • Calvo, R. A., D'Mello, S., Gratch, J., & Kappas, A. (Eds.). (2015). The Oxford handbook of affective computing. Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199942237.001.0001
  • Cartolovni, A., Tomićić, A., & Lazić Mosler, E. (2022). Ethical, legal, and social considerations of AI-based medical decision-support tools: A scoping review. International Journal of Medical Informatics, 161, 104738. https://doi.org/10.1016/j.ijmedinf.2022.104738
  • Chagas, M. E. V., de Oliveira Laguna Silva, G., Ricardo Fernandes, G., Tizianel Aguilar, G., Motta Dias da Silva, M., Moraes, E. L., D Avila Lottici, I., da Rosa de Amorim, J., de Abreu, T., de Campos Moreira, T., & Cezar Cabral, F. (2025). The evolution of digital health: A global, Latin American, and Brazilian bibliometric analysis. Frontiers in Digital Health, 7, 1582719. https://doi.org/10.3389/fdgth.2025.1582719
  • Chen, G., & Xiao, L. (2016). Selecting publication keywords for domain analysis in bibliometrics: A comparison of three methods. Journal of Informetrics, 10(1), 212-223.
  • Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94-98. https://doi.org/10.7861/futurehosp.6-2-94
  • Dearing, J. W., & Cox, J. G. (2018). Diffusion of innovations theory, principles, and practice. Health Affairs, 37(2), 183-190. https://doi.org/10.1377/hlthaff.2017.1104
  • 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
  • Elasan, S. (2023). Bibliometric analyses of digitalization studies in health after the pandemic. Van Tıp Dergisi, 30(3), 300-305. https://doi.org/10.5505/vtd.2023.36158
  • El-Sherif, D. M., Abouzid, M., Elzarif, M. T., Ahmed, A. A., Albakri, A., & Alshehri, M. M. (2022). Telehealth and artificial intelligence insights into healthcare during the COVID-19 pandemic. Healthcare, 10(2), 385. https://doi.org/10.3390/healthcare10020385
  • Fiske, A., Henningsen, P., & Buyx, A. (2019). Your robot therapist will see you now: Ethical implications of embodied artificial intelligence in psychiatry, psychology, and psychotherapy. Journal of Medical Internet Research, 21(5), e13216. https://doi.org/10.2196/13216
  • Fitzpatrick, P. J. (2023). Improving health literacy using the power of digital communications to achieve better health outcomes for patients and practitioners. Frontiers in Digital Health, 5, 1264780. https://doi.org/10.3389/fdgth.2023.1264780
  • Ford, K. L., West, A. B., Bucher, A., & Osborn, C. Y. (2022). Personalized digital health communications to increase COVID-19 vaccination in underserved populations: A double diamond approach to behavioral design. Frontiers in Digital Health, 4, 831093. https://doi.org/10.3389/fdgth.2022.831093
  • García-Avilés, J. A. (2020). Diffusion of innovation. In The International Encyclopedia of Media Psychology. Wiley. https://doi.org/10.1002/9781119011071.iemp0137
  • Gilbert, J.-P., Ng, V., Niu, J., & Rees, E. E. (2020). A call for an ethical framework when using social media data for artificial intelligence applications in public health research. Canada Communicable Disease Report, 46(6), 169-173. https://doi.org/10.14745/ccdr.v46i06a03
  • Glänzel, W. (1996). The need for standards in bibliometric research and technology. Scientometrics, 35(2), 167-176.
  • Goirand, M., Austin, E., & Clay-Williams, R. (2021). Implementing ethics in healthcare AI-based applications: A scoping review. Science and Engineering Ethics, 27, 61. https://doi.org/10.1007/s11948-021-00336-3
  • Grosjean, S., Fox, S., Cherba, M., & Matte, F. (2024). Integrating digital health technologies in clinical practice and everyday life: Unfolding innovative communication practices. Frontiers in Communication, 9, 1426937. https://doi.org/10.3389/fcomm.2024.1426937
  • Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., ... Webster, D. R. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, 316(22), 2402-2410. https://doi.org/10.1001/jama.2016.17216
  • Gulumbe, B. H., Yusuf, Z. M., & Hashim, A. M. (2023). Harnessing artificial intelligence in the post-COVID-19 era: A global health imperative. Tropical Doctor, 53(4), 414-415. https://doi.org/10.1177/00494755231181155
  • Gunasekeran, D. V., Tseng, R. M. W. W., Tham, Y.-C., & Wong, T. Y. (2021). Applications of digital health for public health responses to COVID-19: A systematic scoping review of artificial intelligence, telehealth and related technologies. npj Digital Medicine, 4, 40. https://doi.org/10.1038/s41746-021-00412-9
  • Häfliger, C., Diviani, N., & Rubinelli, S. (2023). Communication inequalities and health disparities among vulnerable groups during the COVID-19 pandemic: A scoping review of qualitative and quantitative evidence. BMC Public Health, 23, 428. https://doi.org/10.1186/s12889-023-15295-6
  • Holden, R. J., & Karsh, B.-T. (2010). The technology acceptance model: Its past and its future in health care. Journal of Biomedical Informatics, 43(1), 159-172. https://doi.org/10.1016/j.jbi.2009.07.002
  • Ikemsi, K. C. (2020). Media and health communication: An overview. European Journal of Social Sciences Studies, 2(1), 95-104. https://doi.org/10.5281/zenodo.3757323
  • Isbanner, S., O'Shaughnessy, P., Steel, D., Wilcock, S., & Carter, S. (2022). The adoption of artificial intelligence in health care and social services in Australia: Findings from a methodologically innovative national survey of values and attitudes (the AVA-AI Study). Journal of Medical Internet Research, 24(8), e37611. https://doi.org/10.2196/37611
  • Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Shen, H., & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2, e000101. https://doi.org/10.1136/svn-2017-000101
  • Jones, C. L., Jensen, J. D., Scherr, C. L., Brown, N. R., Christy, K., & Weaver, J. (2015). The health belief model as an explanatory framework in communication research: Exploring parallel, serial, and moderated mediation. Health Communication, 30(6), 566-576. https://doi.org/10.1080/10410236.2013.873363
  • Khosravi, M., Zare, Z., Mojtabaeian, S. M., & Izadi, R. (2024). Ethical challenges of using artificial intelligence in healthcare delivery: A thematic analysis of a systematic review of reviews. Journal of Public Health. Advance online publication. https://doi.org/10.1007/s10389-024-02219-w
  • Kim, G. J., & Namkoong, K. (2025). Developing the digital health communication maturity model: Systematic review. Journal of Medical Internet Research, 27, e68344. https://doi.org/10.2196/68344
  • Kim, J., & Park, H.-A. (2012). Development of a health information technology acceptance model using consumers' health behavior intention. Journal of Medical Internet Research, 14(5), e133. https://doi.org/10.2196/jmir.2143
  • Komorowski, M., Celi, L. A., Badawi, O., Gordon, A. C., & Faisal, A. A. (2018). The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care. Nature Medicine, 24(11), 1716-1720. https://doi.org/10.1038/s41591-018-0213-5
  • Kreps, G. L. (2012). The maturation of health communication inquiry: Directions for future development and growth. Journal of Health Communication, 17(5), 495-497. https://doi.org/10.1080/10810730.2012.685802
  • Kreps, G. L. (2017). Online information and communication systems to enhance health outcomes through communication convergence. Human Communication Research, 43(4), 518-530. https://doi.org/10.1111/hcre.12117
  • Laranjo, L., Dunn, A. G., Tong, H. L., Kocaballi, A. B., Chen, J., Bashir, R., ... Coiera, E. (2018). Conversational agents in healthcare: A systematic review. Journal of the American Medical Informatics Association, 25(9), 1248-1258. https://doi.org/10.1093/jamia/ocy072
  • Li, Y.-H., Li, Y.-L., Wei, M.-Y., & Li, G.-Y. (2024). Innovation and challenges of artificial intelligence technology in personalized healthcare. Scientific Reports, 14(1), 18994. https://doi.org/10.1038/s41598-024-70073-7
  • Mahmood, S., Hasan, K., Colder Carras, M., & Labrique, A. (2020). Global preparedness against COVID-19: We must leverage the power of digital health. JMIR Public Health and Surveillance, 6(2), e18980. https://doi.org/10.2196/18980
  • Manogaran, G., Varatharajan, R., Lopez, D., Kumar, P. M., Sundarasekar, R., & Thota, C. (2018). A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting system. Future Generation Computer Systems, 82, 375-387. https://doi.org/10.1016/j.future.2017.10.045
  • Miner, A. S., Laranjo, L., & Kocaballi, A. B. (2020). Chatbots in the fight against the COVID-19 pandemic. NPJ Digital Medicine, 3, 65. https://doi.org/10.1038/s41746-020-0280-0
  • Morley, J., Machado, C. C. V., Burr, C., Cowls, J., Josh, I., Taddeo, M., & Floridi, L. (2020). The ethics of AI in health care: A mapping review. Social Science & Medicine, 260, 113172. https://doi.org/10.1016/j.socscimed.2020.113172
  • Mosa, A. S. M., Yoo, I., & Sheets, L. (2012). A systematic review of healthcare applications for smartphones. BMC Medical Informatics and Decision Making, 12, 67. https://doi.org/10.1186/1472-6947-12-67
  • Nasir, S., Khan, R. A., & Bai, S. (2023). Ethical framework for harnessing the power of AI in healthcare and beyond. arXiv Preprint, arXiv:2309.00064. https://doi.org/10.48550/arXiv.2309.00064
  • Natarajan, S., Jain, A., Krishnan, R., Rogye, A., & Sivaprasad, S. (2019). Diagnostic accuracy of community-based diabetic retinopathy screening with an offline artificial intelligence system on a smartphone. JAMA Ophthalmology, 137(10), 1182-1188. https://doi.org/10.1001/jamaophthalmol.2019.2923
  • Nguyen, H.-S., & Voznak, M. (2024). A bibliometric analysis of technology in digital health: Exploring health metaverse and visualizing emerging healthcare management trends. IEEE Access, 12, Article 3363165. https://doi.org/10.1109/ACCESS.2024.3363165
  • Nguyen, M. H., Gruber, J., Fuchs, J., Marler, W., Hunsaker, A., & Hargittai, E. (2020). Changes in digital communication during the COVID-19 global pandemic: Implications for digital inequality and future research. Social Media + Society, 6(3), 1-6. https://doi.org/10.1177/2056305120948255
  • Nguyen, M. H., Hargittai, E., & Marler, W. (2021). Digital inequality in communication during a time of physical distancing: The case of COVID-19. Computers in Human Behavior, 120, 106717. https://doi.org/10.1016/j.chb.2021.106717
  • Olawade, D. B., Wada, O. Z., Odetayo, A., David-Olawade, A. C., Asaolu, F., & Eberhardt, J. (2024). Enhancing mental health with artificial intelligence: Current trends and future prospects. Journal of Medicine, Surgery, and Public Health, 3, 100099. https://doi.org/10.1016/j.glmedi.2024.100099
  • Ong, J. C. L., Seng, B. J. J., Law, J. Z. F., Low, L. L., Kwa, A. L. H., Giacomini, K. M., & Ting, D. S. W. (2024). Artificial intelligence, ChatGPT, and other large language models for social determinants of health: Current state and future directions. Cell Reports Medicine, 5(1), 101356. https://doi.org/10.1016/j.xcrm.2023.101356
  • Paige, S. R., Stellefson, M., Krieger, J. L., Anderson-Lewis, C., Cheong, J., & Stopka, C. (2018). Proposing a transactional model of eHealth literacy: Concept analysis. Journal of Medical Internet Research, 20(10), e10175. https://doi.org/10.2196/10175
  • Parackal, M., Parackal, S. M., Mather, D. W., & Eusebius, S. (2020). Dynamic transactional model: A framework for communicating public health messages via social media. Perspectives in Public Health, 140(4), 207-210. https://doi.org/10.1177/1757913920935910
  • Peek, N., Sujan, M., & Scott, P. (2023). Digital health and care: Emerging from pandemic times. BMJ Health & Care Informatics, 30, e100861. https://doi.org/10.1136/bmjhci-2023-100861
  • Picard, R. W. (2000). Affective computing. MIT press. https://doi.org/10.7551/mitpress/1140.001.0001
  • Reddy, S., Allan, S., Coghlan, S., & Cooper, P. (2020). A governance model for the application of AI in health care. Journal of the American Medical Informatics Association, 27(3), 491-497. https://doi.org/10.1093/jamia/ocz192
  • Robbins, D., & Dunn, P. (2019). Digital health literacy in a person-centric world. International Journal of Cardiology, 290, 154-155. https://doi.org/10.1016/j.ijcard.2019.05.033
  • Rogers, W. A., Carter, S. M., & Entwistle, V. A. (2021). Evaluation of artificial intelligence clinical applications: Detailed case analyses show value of healthcare ethics approach in identifying patient care issues. Bioethics, 35(7), 623-633. https://doi.org/10.1111/bioe.12885
  • Sezgin, E., & Kocaballi, A. B. (2025). Era of generalist conversational artificial intelligence to support public health communications. Journal of Medical Internet Research, 27, e69007. https://doi.org/10.2196/69007
  • Shah, P., Kendall, F., Khozin, S., Goosen, R., Hu, J., Laramie, J., Ringel, M., & Schork, N. (2019). Artificial intelligence and machine learning in clinical development: A translational perspective. NPJ Digital Medicine, 2, 69. https://doi.org/10.1038/s41746-019-0148-3
  • Singhal, A., Neveditsin, N., Tanveer, H., & Mago, V. (2024). Toward fairness, accountability, transparency, and ethics in AI for social media and health care: Scoping review. JMIR Medical Informatics, 12, e50048. https://doi.org/10.2196/50048
  • Singh, V. K., Singh, P., Karmakar, M., Leta, J., & Mayr, P. (2021). The journal coverage of Web of Science, Scopus and Dimensions: A comparative analysis. Scientometrics, 126, 5113-5142. https://doi.org/10.1007/s11192-021-03948-5
  • Song, M., Elson, J., & Bastola, D. (2025). Digital age transformation in patient-physician communication: 25-year narrative review (1999-2023). Journal of Medical Internet Research, 27, e60512. https://doi.org/10.2196/60512
  • Sørensen, K., Okan, O., Kondilis, B., & Levin-Zamir, D. (2021). Rebranding social distancing to physical distancing: Calling for a change in the health promotion vocabulary to enhance clear communication during a pandemic. Global Health Promotion, 28(1), 5-14. https://doi.org/10.1177/1757975920986126
  • Sørensen, K., Van den Broucke, S., Fullam, J., Doyle, G., Pelikan, J., Slonska, Z., & Brand, H. (2012). Health literacy and public health: A systematic review and integration of definitions and models. BMC Public Health, 12, 80. https://doi.org/10.1186/1471-2458-12-80
  • Sun, G., & Zhou, Y.-H. (2023). AI in healthcare: Navigating opportunities and challenges in digital communication. Frontiers in Digital Health, 5, 1291132. https://doi.org/10.3389/fdgth.2023.1291132
  • Thiagarajan, P., & McKimm, J. (2019). Mapping transactional analysis to clinical leadership models. BMJ Leader, 3(2), 77-80.
  • Torous, J., Bucci, S., Bell, I. H., Kessing, L. V., Faurholt-Jepsen, M., Whelan, P., Carvalho, A. F., Keshavan, M., Linardon, J., & Firth, J. (2021). The growing field of digital psychiatry: Current evidence and the future of apps, social media, chatbots, and virtual reality. World Psychiatry, 20(3), 318-335. https://doi.org/10.1002/wps.20883
  • Valente, T. W., & Fosados, R. (2006). Diffusion of innovations and network segmentation: The part played by people in promoting health. Sexually Transmitted Diseases, 33(7 Suppl), S23-S31. https://doi.org/10.1097/01.olq.0000221018.32533.6d
  • van den Eertwegh, V., van Dulmen, S., van Dalen, J., Scherpbier, A. J. J. A., & van der Vleuten, C. P. M. (2013). Learning in context: Identifying gaps in research on the transfer of medical communication skills to the clinical workplace. Patient Education and Counseling, 90(2), 184-192. https://doi.org/10.1016/j.pec.2012.06.008
  • van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538.
  • Wang, Y., McKee, M., Torbica, A., & Stuckler, D. (2019). Systematic literature review on the spread of health-related misinformation on social media. Social Science & Medicine, 240, 112552. https://doi.org/10.1016/j.socscimed.2019.112552
  • Wallin, J. A. (2005). Bibliometric methods: Pitfalls and possibilities. Basic & Clinical Pharmacology & Toxicology, 97(5), 261-275. https://doi.org/10.1111/j.1742-7843.2005.pto_139.x
  • World Health Organization. (2022). Global health statistics report 2022: Monitoring health for the sustainable development goals. WHO Press.
  • Zhang, J., & Zhang, Z.-M. (2023). Ethics and governance of trustworthy medical artificial intelligence. BMC Medical Informatics and Decision Making, 23, 7. https://doi.org/10.1186/s12911-023-02103-9
  • Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429-472.

Dijital Çağda Sağlık İletişimi: 2000-2025 Yılları Arasında Yapay Zekâ Uygulamalarının Bibliyometrik Analizi

Yıl 2025, Cilt: 16 Sayı: 31, 42 - 70, 31.12.2025
https://izlik.org/JA37SS95KW

Öz

Bu çalışma, 2000-2025 yılları arasında Web of Science veri tabanında sağlık iletişimi ve yapay zekâ alanında yayınlanan akademik çalışmaların bibliyometrik analizini sunmaktadır. Araştırma kapsamında 367.665 akademik çalışma incelenmiş, yayın türleri, yıllara göre dağılımı, yazar iş birliği ağları, anahtar kelime eğilimleri ve ülkelere göre dağılımları analiz edilmiştir. Bulgular, sağlık iletişimi alanında yapay zekâ uygulamalarının özellikle 2015 sonrasında hızla arttığını, 2021-2024 yılları arasında en yüksek seviyeye ulaştığını göstermektedir. Çalışmaların yüzde 43,64'ü makale, yüzde 40,55'i bildiri formatındadır. Anahtar kelime analizinde "yapay zekâ" 257 tekrar, "makine öğrenmesi" 223 tekrar ve "derin öğrenme" 151 tekrar ile en sık kullanılan terimler olarak belirlenmiştir. Pandemi sonrası dönemde chatbotlar, doğal dil işleme ve dijital sağlık konularının öne çıktığı tespit edilmiştir. Yazar iş birliği ağlarında Pham Quoc-Viet ve Alazab Mamoun merkezi konumdadır. ABD, İngiltere, Fransa, Almanya, İsveç, İsrail ve İspanya bu alanda en çok yayın yapan ülkelerdir. Sonuçlar, yapay zekâ ve sağlık iletişimi araştırmalarının çok disiplinli bir yapıya kavuştuğunu ortaya koymaktadır. Çalışma, sağlık hizmetlerinin geleceğini şekillendirme potansiyeline sahip olan bu gelişmelerin yanı sıra, etik, mahremiyet ve algoritmik ön yargı gibi konuların da dikkatle ele alınması gerektiğini vurgulamaktadır.

Kaynakça

  • Adnan, A., Irvine, R. E., Williams, A., Harris, M., & Antonacci, G. (2025). Improving acceptability of mHealth apps-The use of the technology acceptance model to assess the acceptability of mHealth apps: Systematic review. Journal of Medical Internet Research, 27, e66432. https://doi.org/10.2196/66432
  • Agarwal, R., Gao, G., DesRoches, C., & Jha, A. K. (2010). Research commentary-The digital transformation of healthcare: current status and the road ahead. Information Systems Research, 21(4), 796-809. https://doi.org/10.1287/isre.1100.0327
  • Albahri, A. S., Duhaim, A. M., Fadhel, M. A., Alnoor, A., Baqer, N. S., Alzubaidi, L., Albahri, O. S., Alamoodi, A. H., Bai, J., Salhi, A., Santamaría, J., Ouyang, C., Gupta, A., Gu, Y., & Deveci, M. (2023). A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion. Information Fusion, 96, 156-191. https://doi.org/10.1016/j.inffus.2023.03.008
  • Amugongo, L. M., Kriebitz, A., Boch, A., & Lütge, C. (2025). Operationalising AI ethics through the agile software development lifecycle: A case study of AI-enabled mobile health applications. AI and Ethics, 5, 227-244. https://doi.org/10.1007/s43681-023-00331-3
  • Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975.
  • Asan, O., Bayrak, A. E., & Choudhury, A. (2020). Artificial intelligence and human trust in healthcare: Focus on clinicians. Journal of Medical Internet Research, 22(6), e15154. https://doi.org/10.2196/15154
  • Badr, J., Motulsky, A., & Denis, J.-L. (2024). Digital health technologies and inequalities: A scoping review of potential impacts and policy recommendations. Health Policy, 146, 105122. https://doi.org/10.1016/j.healthpol.2024.105122
  • Balcombe, L. (2023). AI chatbots in digital mental health. Informatics, 10(4), 82. https://doi.org/10.3390/informatics10040082
  • Berkman, N. D., Sheridan, S. L., Donahue, K. E., Halpern, D. J., & Crotty, K. (2011). Low health literacy and health outcomes: An updated systematic review. Annals of Internal Medicine, 155(2), 97-107. https://doi.org/10.7326/0003-4819-155-2-201107190-00005
  • Bezuidenhout, N. (2024). Digital communication. Medical Writing, 33(2), 74. https://doi.org/10.56012/gsrj5809
  • Bhardwaj, U., H, M., Gambhir, V., Das, A., Sudhir, R., Kaur, A., & Dev, A. (2024). Analyzing the impact of digital health communication on patient engagement and treatment adherence. Seminars in Medical Writing and Education, 3, 492. https://doi.org/10.56294/mw2024492
  • Bidino, R. D., Daugbjerg, S., Papavero, S. C., Haraldsen, I. H., Cicchetti, A., & Sacchini, D. (2024). Health technology assessment framework for artificial intelligence-based technologies. International Journal of Technology Assessment in Health Care, 40(1), e61. https://doi.org/10.1017/S0266462324000308
  • Borg, K., Boulet, M., Smith, L., & Bragge, P. (2018). Digital inclusion & health communication: A rapid review of literature. Health Communication, 33(10), 1237-1249. https://doi.org/10.1080/10410236.2018.1485077
  • Calvo, R. A., D'Mello, S., Gratch, J., & Kappas, A. (Eds.). (2015). The Oxford handbook of affective computing. Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199942237.001.0001
  • Cartolovni, A., Tomićić, A., & Lazić Mosler, E. (2022). Ethical, legal, and social considerations of AI-based medical decision-support tools: A scoping review. International Journal of Medical Informatics, 161, 104738. https://doi.org/10.1016/j.ijmedinf.2022.104738
  • Chagas, M. E. V., de Oliveira Laguna Silva, G., Ricardo Fernandes, G., Tizianel Aguilar, G., Motta Dias da Silva, M., Moraes, E. L., D Avila Lottici, I., da Rosa de Amorim, J., de Abreu, T., de Campos Moreira, T., & Cezar Cabral, F. (2025). The evolution of digital health: A global, Latin American, and Brazilian bibliometric analysis. Frontiers in Digital Health, 7, 1582719. https://doi.org/10.3389/fdgth.2025.1582719
  • Chen, G., & Xiao, L. (2016). Selecting publication keywords for domain analysis in bibliometrics: A comparison of three methods. Journal of Informetrics, 10(1), 212-223.
  • Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94-98. https://doi.org/10.7861/futurehosp.6-2-94
  • Dearing, J. W., & Cox, J. G. (2018). Diffusion of innovations theory, principles, and practice. Health Affairs, 37(2), 183-190. https://doi.org/10.1377/hlthaff.2017.1104
  • 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
  • Elasan, S. (2023). Bibliometric analyses of digitalization studies in health after the pandemic. Van Tıp Dergisi, 30(3), 300-305. https://doi.org/10.5505/vtd.2023.36158
  • El-Sherif, D. M., Abouzid, M., Elzarif, M. T., Ahmed, A. A., Albakri, A., & Alshehri, M. M. (2022). Telehealth and artificial intelligence insights into healthcare during the COVID-19 pandemic. Healthcare, 10(2), 385. https://doi.org/10.3390/healthcare10020385
  • Fiske, A., Henningsen, P., & Buyx, A. (2019). Your robot therapist will see you now: Ethical implications of embodied artificial intelligence in psychiatry, psychology, and psychotherapy. Journal of Medical Internet Research, 21(5), e13216. https://doi.org/10.2196/13216
  • Fitzpatrick, P. J. (2023). Improving health literacy using the power of digital communications to achieve better health outcomes for patients and practitioners. Frontiers in Digital Health, 5, 1264780. https://doi.org/10.3389/fdgth.2023.1264780
  • Ford, K. L., West, A. B., Bucher, A., & Osborn, C. Y. (2022). Personalized digital health communications to increase COVID-19 vaccination in underserved populations: A double diamond approach to behavioral design. Frontiers in Digital Health, 4, 831093. https://doi.org/10.3389/fdgth.2022.831093
  • García-Avilés, J. A. (2020). Diffusion of innovation. In The International Encyclopedia of Media Psychology. Wiley. https://doi.org/10.1002/9781119011071.iemp0137
  • Gilbert, J.-P., Ng, V., Niu, J., & Rees, E. E. (2020). A call for an ethical framework when using social media data for artificial intelligence applications in public health research. Canada Communicable Disease Report, 46(6), 169-173. https://doi.org/10.14745/ccdr.v46i06a03
  • Glänzel, W. (1996). The need for standards in bibliometric research and technology. Scientometrics, 35(2), 167-176.
  • Goirand, M., Austin, E., & Clay-Williams, R. (2021). Implementing ethics in healthcare AI-based applications: A scoping review. Science and Engineering Ethics, 27, 61. https://doi.org/10.1007/s11948-021-00336-3
  • Grosjean, S., Fox, S., Cherba, M., & Matte, F. (2024). Integrating digital health technologies in clinical practice and everyday life: Unfolding innovative communication practices. Frontiers in Communication, 9, 1426937. https://doi.org/10.3389/fcomm.2024.1426937
  • Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., ... Webster, D. R. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, 316(22), 2402-2410. https://doi.org/10.1001/jama.2016.17216
  • Gulumbe, B. H., Yusuf, Z. M., & Hashim, A. M. (2023). Harnessing artificial intelligence in the post-COVID-19 era: A global health imperative. Tropical Doctor, 53(4), 414-415. https://doi.org/10.1177/00494755231181155
  • Gunasekeran, D. V., Tseng, R. M. W. W., Tham, Y.-C., & Wong, T. Y. (2021). Applications of digital health for public health responses to COVID-19: A systematic scoping review of artificial intelligence, telehealth and related technologies. npj Digital Medicine, 4, 40. https://doi.org/10.1038/s41746-021-00412-9
  • Häfliger, C., Diviani, N., & Rubinelli, S. (2023). Communication inequalities and health disparities among vulnerable groups during the COVID-19 pandemic: A scoping review of qualitative and quantitative evidence. BMC Public Health, 23, 428. https://doi.org/10.1186/s12889-023-15295-6
  • Holden, R. J., & Karsh, B.-T. (2010). The technology acceptance model: Its past and its future in health care. Journal of Biomedical Informatics, 43(1), 159-172. https://doi.org/10.1016/j.jbi.2009.07.002
  • Ikemsi, K. C. (2020). Media and health communication: An overview. European Journal of Social Sciences Studies, 2(1), 95-104. https://doi.org/10.5281/zenodo.3757323
  • Isbanner, S., O'Shaughnessy, P., Steel, D., Wilcock, S., & Carter, S. (2022). The adoption of artificial intelligence in health care and social services in Australia: Findings from a methodologically innovative national survey of values and attitudes (the AVA-AI Study). Journal of Medical Internet Research, 24(8), e37611. https://doi.org/10.2196/37611
  • Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Shen, H., & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2, e000101. https://doi.org/10.1136/svn-2017-000101
  • Jones, C. L., Jensen, J. D., Scherr, C. L., Brown, N. R., Christy, K., & Weaver, J. (2015). The health belief model as an explanatory framework in communication research: Exploring parallel, serial, and moderated mediation. Health Communication, 30(6), 566-576. https://doi.org/10.1080/10410236.2013.873363
  • Khosravi, M., Zare, Z., Mojtabaeian, S. M., & Izadi, R. (2024). Ethical challenges of using artificial intelligence in healthcare delivery: A thematic analysis of a systematic review of reviews. Journal of Public Health. Advance online publication. https://doi.org/10.1007/s10389-024-02219-w
  • Kim, G. J., & Namkoong, K. (2025). Developing the digital health communication maturity model: Systematic review. Journal of Medical Internet Research, 27, e68344. https://doi.org/10.2196/68344
  • Kim, J., & Park, H.-A. (2012). Development of a health information technology acceptance model using consumers' health behavior intention. Journal of Medical Internet Research, 14(5), e133. https://doi.org/10.2196/jmir.2143
  • Komorowski, M., Celi, L. A., Badawi, O., Gordon, A. C., & Faisal, A. A. (2018). The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care. Nature Medicine, 24(11), 1716-1720. https://doi.org/10.1038/s41591-018-0213-5
  • Kreps, G. L. (2012). The maturation of health communication inquiry: Directions for future development and growth. Journal of Health Communication, 17(5), 495-497. https://doi.org/10.1080/10810730.2012.685802
  • Kreps, G. L. (2017). Online information and communication systems to enhance health outcomes through communication convergence. Human Communication Research, 43(4), 518-530. https://doi.org/10.1111/hcre.12117
  • Laranjo, L., Dunn, A. G., Tong, H. L., Kocaballi, A. B., Chen, J., Bashir, R., ... Coiera, E. (2018). Conversational agents in healthcare: A systematic review. Journal of the American Medical Informatics Association, 25(9), 1248-1258. https://doi.org/10.1093/jamia/ocy072
  • Li, Y.-H., Li, Y.-L., Wei, M.-Y., & Li, G.-Y. (2024). Innovation and challenges of artificial intelligence technology in personalized healthcare. Scientific Reports, 14(1), 18994. https://doi.org/10.1038/s41598-024-70073-7
  • Mahmood, S., Hasan, K., Colder Carras, M., & Labrique, A. (2020). Global preparedness against COVID-19: We must leverage the power of digital health. JMIR Public Health and Surveillance, 6(2), e18980. https://doi.org/10.2196/18980
  • Manogaran, G., Varatharajan, R., Lopez, D., Kumar, P. M., Sundarasekar, R., & Thota, C. (2018). A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting system. Future Generation Computer Systems, 82, 375-387. https://doi.org/10.1016/j.future.2017.10.045
  • Miner, A. S., Laranjo, L., & Kocaballi, A. B. (2020). Chatbots in the fight against the COVID-19 pandemic. NPJ Digital Medicine, 3, 65. https://doi.org/10.1038/s41746-020-0280-0
  • Morley, J., Machado, C. C. V., Burr, C., Cowls, J., Josh, I., Taddeo, M., & Floridi, L. (2020). The ethics of AI in health care: A mapping review. Social Science & Medicine, 260, 113172. https://doi.org/10.1016/j.socscimed.2020.113172
  • Mosa, A. S. M., Yoo, I., & Sheets, L. (2012). A systematic review of healthcare applications for smartphones. BMC Medical Informatics and Decision Making, 12, 67. https://doi.org/10.1186/1472-6947-12-67
  • Nasir, S., Khan, R. A., & Bai, S. (2023). Ethical framework for harnessing the power of AI in healthcare and beyond. arXiv Preprint, arXiv:2309.00064. https://doi.org/10.48550/arXiv.2309.00064
  • Natarajan, S., Jain, A., Krishnan, R., Rogye, A., & Sivaprasad, S. (2019). Diagnostic accuracy of community-based diabetic retinopathy screening with an offline artificial intelligence system on a smartphone. JAMA Ophthalmology, 137(10), 1182-1188. https://doi.org/10.1001/jamaophthalmol.2019.2923
  • Nguyen, H.-S., & Voznak, M. (2024). A bibliometric analysis of technology in digital health: Exploring health metaverse and visualizing emerging healthcare management trends. IEEE Access, 12, Article 3363165. https://doi.org/10.1109/ACCESS.2024.3363165
  • Nguyen, M. H., Gruber, J., Fuchs, J., Marler, W., Hunsaker, A., & Hargittai, E. (2020). Changes in digital communication during the COVID-19 global pandemic: Implications for digital inequality and future research. Social Media + Society, 6(3), 1-6. https://doi.org/10.1177/2056305120948255
  • Nguyen, M. H., Hargittai, E., & Marler, W. (2021). Digital inequality in communication during a time of physical distancing: The case of COVID-19. Computers in Human Behavior, 120, 106717. https://doi.org/10.1016/j.chb.2021.106717
  • Olawade, D. B., Wada, O. Z., Odetayo, A., David-Olawade, A. C., Asaolu, F., & Eberhardt, J. (2024). Enhancing mental health with artificial intelligence: Current trends and future prospects. Journal of Medicine, Surgery, and Public Health, 3, 100099. https://doi.org/10.1016/j.glmedi.2024.100099
  • Ong, J. C. L., Seng, B. J. J., Law, J. Z. F., Low, L. L., Kwa, A. L. H., Giacomini, K. M., & Ting, D. S. W. (2024). Artificial intelligence, ChatGPT, and other large language models for social determinants of health: Current state and future directions. Cell Reports Medicine, 5(1), 101356. https://doi.org/10.1016/j.xcrm.2023.101356
  • Paige, S. R., Stellefson, M., Krieger, J. L., Anderson-Lewis, C., Cheong, J., & Stopka, C. (2018). Proposing a transactional model of eHealth literacy: Concept analysis. Journal of Medical Internet Research, 20(10), e10175. https://doi.org/10.2196/10175
  • Parackal, M., Parackal, S. M., Mather, D. W., & Eusebius, S. (2020). Dynamic transactional model: A framework for communicating public health messages via social media. Perspectives in Public Health, 140(4), 207-210. https://doi.org/10.1177/1757913920935910
  • Peek, N., Sujan, M., & Scott, P. (2023). Digital health and care: Emerging from pandemic times. BMJ Health & Care Informatics, 30, e100861. https://doi.org/10.1136/bmjhci-2023-100861
  • Picard, R. W. (2000). Affective computing. MIT press. https://doi.org/10.7551/mitpress/1140.001.0001
  • Reddy, S., Allan, S., Coghlan, S., & Cooper, P. (2020). A governance model for the application of AI in health care. Journal of the American Medical Informatics Association, 27(3), 491-497. https://doi.org/10.1093/jamia/ocz192
  • Robbins, D., & Dunn, P. (2019). Digital health literacy in a person-centric world. International Journal of Cardiology, 290, 154-155. https://doi.org/10.1016/j.ijcard.2019.05.033
  • Rogers, W. A., Carter, S. M., & Entwistle, V. A. (2021). Evaluation of artificial intelligence clinical applications: Detailed case analyses show value of healthcare ethics approach in identifying patient care issues. Bioethics, 35(7), 623-633. https://doi.org/10.1111/bioe.12885
  • Sezgin, E., & Kocaballi, A. B. (2025). Era of generalist conversational artificial intelligence to support public health communications. Journal of Medical Internet Research, 27, e69007. https://doi.org/10.2196/69007
  • Shah, P., Kendall, F., Khozin, S., Goosen, R., Hu, J., Laramie, J., Ringel, M., & Schork, N. (2019). Artificial intelligence and machine learning in clinical development: A translational perspective. NPJ Digital Medicine, 2, 69. https://doi.org/10.1038/s41746-019-0148-3
  • Singhal, A., Neveditsin, N., Tanveer, H., & Mago, V. (2024). Toward fairness, accountability, transparency, and ethics in AI for social media and health care: Scoping review. JMIR Medical Informatics, 12, e50048. https://doi.org/10.2196/50048
  • Singh, V. K., Singh, P., Karmakar, M., Leta, J., & Mayr, P. (2021). The journal coverage of Web of Science, Scopus and Dimensions: A comparative analysis. Scientometrics, 126, 5113-5142. https://doi.org/10.1007/s11192-021-03948-5
  • Song, M., Elson, J., & Bastola, D. (2025). Digital age transformation in patient-physician communication: 25-year narrative review (1999-2023). Journal of Medical Internet Research, 27, e60512. https://doi.org/10.2196/60512
  • Sørensen, K., Okan, O., Kondilis, B., & Levin-Zamir, D. (2021). Rebranding social distancing to physical distancing: Calling for a change in the health promotion vocabulary to enhance clear communication during a pandemic. Global Health Promotion, 28(1), 5-14. https://doi.org/10.1177/1757975920986126
  • Sørensen, K., Van den Broucke, S., Fullam, J., Doyle, G., Pelikan, J., Slonska, Z., & Brand, H. (2012). Health literacy and public health: A systematic review and integration of definitions and models. BMC Public Health, 12, 80. https://doi.org/10.1186/1471-2458-12-80
  • Sun, G., & Zhou, Y.-H. (2023). AI in healthcare: Navigating opportunities and challenges in digital communication. Frontiers in Digital Health, 5, 1291132. https://doi.org/10.3389/fdgth.2023.1291132
  • Thiagarajan, P., & McKimm, J. (2019). Mapping transactional analysis to clinical leadership models. BMJ Leader, 3(2), 77-80.
  • Torous, J., Bucci, S., Bell, I. H., Kessing, L. V., Faurholt-Jepsen, M., Whelan, P., Carvalho, A. F., Keshavan, M., Linardon, J., & Firth, J. (2021). The growing field of digital psychiatry: Current evidence and the future of apps, social media, chatbots, and virtual reality. World Psychiatry, 20(3), 318-335. https://doi.org/10.1002/wps.20883
  • Valente, T. W., & Fosados, R. (2006). Diffusion of innovations and network segmentation: The part played by people in promoting health. Sexually Transmitted Diseases, 33(7 Suppl), S23-S31. https://doi.org/10.1097/01.olq.0000221018.32533.6d
  • van den Eertwegh, V., van Dulmen, S., van Dalen, J., Scherpbier, A. J. J. A., & van der Vleuten, C. P. M. (2013). Learning in context: Identifying gaps in research on the transfer of medical communication skills to the clinical workplace. Patient Education and Counseling, 90(2), 184-192. https://doi.org/10.1016/j.pec.2012.06.008
  • van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538.
  • Wang, Y., McKee, M., Torbica, A., & Stuckler, D. (2019). Systematic literature review on the spread of health-related misinformation on social media. Social Science & Medicine, 240, 112552. https://doi.org/10.1016/j.socscimed.2019.112552
  • Wallin, J. A. (2005). Bibliometric methods: Pitfalls and possibilities. Basic & Clinical Pharmacology & Toxicology, 97(5), 261-275. https://doi.org/10.1111/j.1742-7843.2005.pto_139.x
  • World Health Organization. (2022). Global health statistics report 2022: Monitoring health for the sustainable development goals. WHO Press.
  • Zhang, J., & Zhang, Z.-M. (2023). Ethics and governance of trustworthy medical artificial intelligence. BMC Medical Informatics and Decision Making, 23, 7. https://doi.org/10.1186/s12911-023-02103-9
  • Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429-472.
Toplam 84 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular İletişim Çalışmaları
Bölüm Araştırma Makalesi
Yazarlar

Akin Ay 0000-0002-5936-2722

Serhat Bekar 0000-0002-3322-4559

Gönderilme Tarihi 28 Nisan 2025
Kabul Tarihi 29 Kasım 2025
Yayımlanma Tarihi 31 Aralık 2025
IZ https://izlik.org/JA37SS95KW
Yayımlandığı Sayı Yıl 2025 Cilt: 16 Sayı: 31

Kaynak Göster

APA Ay, A., & Bekar, S. (2025). Dijital Çağda Sağlık İletişimi: 2000-2025 Yılları Arasında Yapay Zekâ Uygulamalarının Bibliyometrik Analizi. Global Media Journal Turkish Edition, 16(31), 42-70. https://izlik.org/JA37SS95KW