Artificial Intelligence in Health Tourism: A Bibliometric Analysis

Nurperihan Tosun; Yılmaz Arık; Abdulkadir Güner

doi:10.59372/turajas.1825150

TR EN

Öz

Sağlık Turizminde Yapay Zekâ: Bibliyometrik Bir Analiz

Öz

Teknolojik gelişmelerin hız kazanmasıyla birlikte, sağlık turizmi ile yapay zekâ (YZ) arasındaki ilişki hem akademik hem de uygulamaya yönelik açıdan giderek daha fazla önem kazanmaktadır. Sağlık turizmi alanında yapay zekâ destekli teknolojiler; pazarlama stratejilerinin geliştirilmesi, hasta deneyimi ve hizmet kalitesinin iyileştirilmesi ile sağlık hizmetlerinde operasyonel verimliliğin artırılmasında kritik bir rol oynamaktadır. Bu çalışmanın amacı, yapay zekâ destekli sağlık turizmi alanında gerçekleştirilen akademik araştırmaları bibliyometrik analiz yöntemiyle incelemektir. Çalışma kapsamında Web of Science (WoS) veritabanında indekslenen yayınlar analiz edilerek; yıllara göre yayın dağılımı, öne çıkan yazarlar ve ülkeler, iş birliği ağları, etkili dergiler, sık kullanılan anahtar kelimeler ve gelişen araştırma eğilimleri değerlendirilmiştir. Bulgular, yapay zekâ destekli sağlık turizmi alanındaki akademik yayınların WoS veritabanında 1990 yılından itibaren yer aldığını ve özellikle 2019 sonrasında belirgin bir artış gösterdiğini ortaya koymaktadır. En yüksek yayın sayısına 2024 yılında ulaşılmış ve bu yılda toplam 50 çalışma yayımlanmıştır. Toplamda 207 çalışma, 177 farklı kaynakta yayımlanmış olup, yayınların yıllık ortalama büyüme oranı %5,72 olarak hesaplanmıştır. Çalışmalara 1.037 araştırmacı katkı sağlamış ve yayın başına ortalama yazar sayısı 5,22 olarak belirlenmiştir; bu durum, alanın güçlü bir iş birliği yapısına sahip olduğunu göstermektedir. Sensors dergisi en üretken kaynak olarak öne çıkarken, Artificial Intelligence in Medicine en çok atıf alan dergiler arasında yer almıştır. Ülkelere göre değerlendirildiğinde, en fazla yayının Amerika Birleşik Devletleri kaynaklı olduğu, bunu Çin ve İtalya’nın izlediği görülmüştür. Anahtar kelime analizi sonucunda, çalışmalarda en sık kullanılan kavramların yapay zekâ, sistem, model, internet, tanı ve wellness olduğu belirlenmiştir. 2019 sonrası dönemde yapay zekâ odaklı çalışmaların artış göstermesi, bu teknolojinin sağlık turizmi alanında giderek daha merkezi bir araştırma konusu hâline geldiğini ortaya koymaktadır. Elde edilen bulgular, yapay zekâ destekli sağlık turizminin gelecekte de artan akademik ilgi göreceğini ve sektörün şekillenmesinde önemli bir rol üstleneceğini göstermektedir.

Anahtar Kelimeler

Artificial Intelligence in Health Tourism: A Bibliometric Analysis

Öz

The rapid advancement of technology has strengthened the relationship between health tourism and artificial intelligence (AI), making this field increasingly important from both academic and practical perspectives. In the context of health tourism, AI-supported technologies play a critical role in developing marketing strategies, enhancing patient experience and service quality, and improving operational efficiency in healthcare services. This study aims to examine academic research on AI-supported health tourism through a bibliometric analysis. Within the scope of the study, publications indexed in the Web of Science (WoS) database were analyzed in terms of annual scientific production, leading authors and countries, collaboration networks, influential journals, frequently used keywords, and emerging research trends. The findings indicate that academic publications on AI-supported health tourism have been indexed in the WoS database since 1990 and have shown a marked increase, particularly after 2019. The highest number of publications was reached in 2024, with a total of 50 studies published. In total, 207 studies were published across 177 different sources, with an average annual growth rate of 5.72%. A total of 1,037 researchers contributed to these publications, with an average of 5.22 authors per study, indicating a strong collaborative research structure. Sensors emerged as the most productive journal, while Artificial Intelligence in Medicine was among the most highly cited sources. In terms of geographical distribution, researchers from the United States produced the highest number of publications, followed by China and Italy. Keyword analysis revealed that the most frequently used terms included artificial intelligence, system, model, internet, diagnosis, and wellness. The significant increase in AI-related studies after 2019 highlights the growing prominence of artificial intelligence as a central research theme within health tourism. Overall, the findings suggest that AI-supported health tourism will continue to attract increasing academic attention and will play a pivotal role in shaping the future of the sector.

Anahtar Kelimeler

Kaynakça

Abanga, E. A., & Acquah, T. (2024). A Bibliometric Analysis of Global Research Trends in Artificial Intelligence from 2019 to 2023. Asian Journal of Research in Computer Science, 17(12), 220–233.
Abràmoff, M.D., Tarver, M.E., Loyo-Berrios, N. et al. (2023). Considerations for addressing bias in artificial intelligence for health equity. Npj Digital Medicine, 6, 170. DOI 10.1038/S41746-023-00913-9
Ağaoğlu, F. O., Ekinci, L. O., & Tosun, N. (2022). Metaverse ve Sağlik Hizmetleri Üzerine Bir Değerlendirme. Erzincan Binali Yıldırım Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 4(1), 95-102.
Akalın, B., & Demirbaş, M. B. (2024). Sağlık turizminde web 5.0’a doğru gelişmeler: Sistematik derleme. Sağlık ve Sosyal Refah Araştırmaları Dergisi, 6(1), 48–65.
Alowais, S.A., Alghamdi, S.S., Alsuhebany, N. et al. (2023). Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Medical Education, 23, 689. DOI 10.1186/S12909-023-04698-Z
Amouzagar, S., Mojaradi, Z., Izanloo, A., Beikzadeh, S., & Milani, M. (2016). Qualitative examination of health tourism and its challenges. International Journal of Travel Medicine and Global Health, 4(3), 88–91.
Aria, M., & Cuccurullo, C. (2017). A brief introduction to bibliometrix. Journal of Informetrics, 11(4), 959–975.
Barua, R., & Datta, S. (2024). The Improvements in Significance of AI in Experiential and Medical Tourism. Advances in Logistics, Operations, and Management Science, 303–334. https://doi.org/10.4018/979-8-3693-5578-7.ch012

Bathla, G., Raina, A., & Rana, V. S. (2024). Artificial Intelligence-Driven Enhancements in Medical Tourism (pp. 139–162). IGI Global. https://doi.org/10.4018/979-8-3693-2248-2.ch006
Bressem K. K, Vahldiek J. L., Adams L., Niehues S. M., Haibel H., Rodriguez V. R., Torgutalp M., Protopopov M., Proft F., Rademacher J., Sieper J., Rudwaleit M., Hamm B., Makowski M. R., Hermann K. G., Poddubnyy D. (2021). Deep learning for detection of radiographic sacroiliitis: achieving expert-level performance. Arthritis Research & Therapy, 23(1), 106. DOI 10.1186/S13075-021-02484-0
Burke, R. (2002). Hybrid Recommender Systems: Survey and Experiments. User Modeing andl User-Adapted Interaction, 12, 331–370. DOI 10.1023/A:1021240730564
Chen, J., Wu, X., & Lai, I. K. W. (2023). A systematic literature review of virtual technology in hospitality and tourism (2013–2022). Sage Open, 13(3), 21582440231193297.
Cheng, S. (2023). Metaverse: Concept, Content and Context (pp. 1–23). Springer Nature Switzerland. Connell, J. (2013). Contemporary medical tourism: Conceptualisation, culture and commodification. Tourism Management, 34, 1–13.
Dwivedi, Y. K., Pandey, N., Currie, W., & Micu, A. (2024). Leveraging ChatGPT and other generative artificial intelligence (AI)-based applications in the hospitality and tourism industry: Practices, challenges and research agenda. International Journal of Contemporary Hospitality Management, 36(1), 1–12.
Ellegaard, O., & Wallin, J. A. (2015). The bibliometric analysis of scholarly production: How great is the impact? Scientometrics, 105(3), 1809–1831.
Fitzpatrick K., Darcy A., Vierhile M. (2017). Delivering Cognitive Behavior Therapy to Young Adults With Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot): A Randomized Controlled Trial. JMIR Ment Health, 4 (2), 19. DOI 10.2196/MENTAL.7785
Gadekallu, T. R., Huynh-The, T., Wang, W., Yenduri, G., Ranaweera, P., Pham, Q. V., ... & Liyanage, M. (2022). Blockchain for the metaverse: A review.
Gupta, O. J., Yadav, S., Srivastava, M. K., Darda, P., & Mishra, V. (2024). Understanding the intention to use metaverse in healthcare utilizing a mix method approach. International Journal of Healthcare Management, 17(2), 318–329.
Haleem A., Javaid M., Singh R. P., Suman R. (2021) Telemedicine for healthcare: Capabilities, features, barriers, and applications. Sensors International. 2, 100117. DOI 10.1016/J.SINTL.2021.100117
Hamet, P., & Tremblay, J. (2017). Artificial intelligence in medicine. Metabolism, 69, 36–40.
Hussain, W., Mabrok, M., Gao, H., Rabhi, F. A., & Rashed, E. A. (2024). Revolutionising healthcare with artificial intelligence: A bibliometric analysis of 40 years of progress in health systems. Digital Health, 10, 20552076241258757.
Huynh-The, T., Pham, Q. V., Pham, X. Q., Nguyen, T. T., Han, Z., & Kim, D. S. (2023). Artificial intelligence for the metaverse: A survey. Engineering Applications of Artificial Intelligence, 117, 105581.
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., ... & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4).
Karcıoğlu, U. B. (2025). The impact of artificial intelligence on the patient journey in medical tourism: A management framework. Eurasian Journal of Health Technology Assessment, 9(1), 58–67. https://doi.org/10.52148/ehta.1701664
Katz, J. S., & Martin, B. R. (1997). What is research collaboration? Research Policy, 26(1), 1–18.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). Gradient-based learning applied to document recognition. Commun. Acm, 60, 84-90. DOI 10.1145/3065386
LeCun, Y., Bengio, Y. and Hinton, G. (2015). Deep learning. Nature, 521, 436–444. DOI 10.1038/NATURE14539
Leydesdorff, L., & Wagner, C. (2008). International collaboration in science and the formation of a core group. Journal of Informetrics, 2(4), 317–325.
Li, Y., Gunasekeran, D. V., RaviChandran, N., Tan, T. F., Ong, J. C. L., Thirunavukarasu, A. J., ... & Ting, D. S. (2024). The next generation of healthcare ecosystem in the metaverse. Biomedical Journal, 47(3), 100679.
Ismail, A., Munsi, H., Yusuf, A. M., & Hijjang, P. (2025). Mapping one decade of identity studies: a comprehensive bibliometric analysis of global trends and scholarly impact. Social Sciences, 14(2), 92.
Ma, S., & Zhang, L. (2024). Enhancing tourists’ satisfaction: Leveraging artificial intelligence in the tourism sector. Pacific International Journal, 7(3), 89–98.
Maksymowych, W. P., Lambert, R. G., Baraliakos, X., Weber, U., Machado, P. M., Pedersen, S. J., ... and Ostergaard, M. (2021). Data-driven definitions for active and structural MRI lesions in the sacroiliac joint in spondyloarthritis and their predictive utility. Rheumatology, 60 (10), 4778-4789. DOI 10.1093/RHEUMATOLOGY/KEAB099
Nair, P. (2024, April 8). Augmenting medical tourism with the metaverse. https://insights.omnia-health.com/technology/augmenting-medical-tourism-metaverse
Saleh, M. I. (2025). Generative artificial intelligence in hospitality and tourism: Future capabilities, AI prompts and real-world applications. Journal of Hospitality Marketing & Management, 34(4), 467–498.
Sharma, S., Chauhan, Y., & Tyagi, R. (2023). Artificial Intelligence–based applications in medical tourism. In 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1–6). IEEE.
Smith, M. (2015). Baltic health tourism: Uniqueness and commonalities. Scandinavian Journal of Hospitality and Tourism, 15(4), 357–379.
Vention. (2025, March 8). AI in healthcare: 2024 stats explained. https://ventionteams.com/healthtech/ai/statistics
Weißensteiner, A. A. A. (2018). Chatbots as an approach for a faster enquiry-handling process in the service industry. Signature, 12(04).
Yadigarova, L. (2025). Bibliometric Analysis of the Impact of International Research Collaborations: Insights from Social Sciences. International Scientific Journal of Universities and Leadership, (19), 37-48.
Yılmaz, F., Mete, A. H., Türkön, B. F., & İnce, Ö. (2022). Sağlık hizmetlerinin geleceğinde metaverse ekosistemi ve teknolojileri: uygulamalar, fırsatlar ve zorluklar. Eurasian Journal of Health Technology Assessment, 6(1), 12–34.
Xu, L., Sanders, L., Li, K., & Chow, J. C. (2021). Chatbot for health care and oncology applications using artificial intelligence and machine learning: systematic review. JMIR Cancer, 7(4), e27850. DOI 10.2196/27850

Ayrıntılar

Birincil Dil

İngilizce

Konular

Sağlık Yönetimi

Bölüm

Araştırma Makalesi

Yazarlar

Nurperihan Tosun ^*
0000-0001-6548-3099
Türkiye

Yılmaz Arık
0000-0002-9953-1607
Türkiye

Abdulkadir Güner
0009-0003-9387-2501
Türkiye

Yayımlanma Tarihi

3 Ocak 2026

Gönderilme Tarihi

17 Kasım 2025

Kabul Tarihi

14 Aralık 2025

Yayımlandığı Sayı

Yıl 2025 Cilt: 8 Sayı: 2

DOI

https://doi.org/10.59372/turajas.1825150

IZ

https://izlik.org/JA45RS55YW

Kaynak Göster

RIS / Bibtex

APA

Tosun, N., Arık, Y., & Güner, A. (2026). Artificial Intelligence in Health Tourism: A Bibliometric Analysis. Turkish Research Journal of Academic Social Science, 8(2), 213-221. https://doi.org/10.59372/turajas.1825150

AMA

1.Tosun N, Arık Y, Güner A. Artificial Intelligence in Health Tourism: A Bibliometric Analysis. TURAJAS. 2026;8(2):213-221. doi:10.59372/turajas.1825150

Chicago

Tosun, Nurperihan, Yılmaz Arık, ve Abdulkadir Güner. 2026. “Artificial Intelligence in Health Tourism: A Bibliometric Analysis”. Turkish Research Journal of Academic Social Science 8 (2): 213-21. https://doi.org/10.59372/turajas.1825150.

EndNote

Tosun N, Arık Y, Güner A (01 Ocak 2026) Artificial Intelligence in Health Tourism: A Bibliometric Analysis. Turkish Research Journal of Academic Social Science 8 2 213–221.

IEEE

[1]N. Tosun, Y. Arık, ve A. Güner, “Artificial Intelligence in Health Tourism: A Bibliometric Analysis”, TURAJAS, c. 8, sy 2, ss. 213–221, Oca. 2026, doi: 10.59372/turajas.1825150.

ISNAD

Tosun, Nurperihan - Arık, Yılmaz - Güner, Abdulkadir. “Artificial Intelligence in Health Tourism: A Bibliometric Analysis”. Turkish Research Journal of Academic Social Science 8/2 (01 Ocak 2026): 213-221. https://doi.org/10.59372/turajas.1825150.

JAMA

1.Tosun N, Arık Y, Güner A. Artificial Intelligence in Health Tourism: A Bibliometric Analysis. TURAJAS. 2026;8:213–221.

MLA

Tosun, Nurperihan, vd. “Artificial Intelligence in Health Tourism: A Bibliometric Analysis”. Turkish Research Journal of Academic Social Science, c. 8, sy 2, Ocak 2026, ss. 213-21, doi:10.59372/turajas.1825150.

Vancouver

1.Nurperihan Tosun, Yılmaz Arık, Abdulkadir Güner. Artificial Intelligence in Health Tourism: A Bibliometric Analysis. TURAJAS. 01 Ocak 2026;8(2):213-21. doi:10.59372/turajas.1825150

Cited By

Physicians’ attitudes toward artificial intelligence and ChatGPT: a cross-sectional study

Journal of Health Organization and Management

https://doi.org/10.1108/JHOM-12-2025-0889