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A BIOMETRIC ANALYSIS OF THE USE OF ARTIFICIAL INTELLIGENCE IN HEALTH SERVICES

Year 2025, Volume: 5 Issue: 2, 11 - 20, 29.08.2025

Abstract

Purpose: This study aims to present a bibliometric analysis by examining 487 sources from which Scopus data was obtained between 1995-2024 in the use of artificial intelligence (AI) in healthcare services in Türkiye. It evaluates trends and focused research by determining influential publications, authors, companies and keywords in the literature, and aims to contribute to Turkey's healthcare research system.
Method: In the search made with the keywords "artificial intelligence" and "health services" in Scopus, 31,668 results were obtained, and it was reduced to 487 sources by limiting it to Turkey. These sources (316 articles, 61 book chapters, 57 accepted articles, 39 reports, 7 editorials, 4 documents, 3 books) were analyzed with the Woswiver program. Bibliometric analysis was performed on author, citation, keyword, journal and institution data.
Findings: In Türkiye, AI-focused health research has intensified in the last 10 years, especially in 2023 (134 publications). While articles (64.9%) stand out in publication types, Pamucar (6 publications) and Kahraman, Yüksel (5 publications each) are the most productive authors. Hacettepe University (29 publications) is the leading institution. Computer science (21%) and medicine (16.9%) are comprehensive fields of study. AI is effective in diagnosis, treatment, preventive health and management, and its usage features with the Covid-19 pandemic. Conclusion: AI plays a transformative role in healthcare in Turkey. However, problems such as infrastructure deficiencies, data security and ethical issues continue. In the future, the durability of AI in healthcare can be increased in a durable and liberal way with the infrastructure investments of the Words, ethical systems and interdisciplinary studies.

References

  • 1. Hamet, P., & Tremblay, J. (2017). Artificial intelligence in medicine. Metabolism, 69(Suppl.), S36–S40. https://doi.org/10.1016/j.metabol.2017.01.011
  • 2. Secinaro, S., Calandra, D., Secinaro, A., Muthurangu,v., & Biancone, P. (2021). The role of artificial intelligence in healthcare: A structured literature review. BMC Medical Informatics and Decision Making, 21(1), Article 125. https://doi.org/10.1186/s12911-021-01488-9
  • 3. World Health Organization. (2000). The world health report 2000: Health systems: Improving performance. Geneva, Switzerland: WHO.
  • 4. Donabedian, A. (1988). The quality of care: How can it be assessed? Journal of the American Medical Association, 260(12), 1743–1748. https://doi.org/10.1001/jama.1988.03410120089033
  • 5. Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., & Kitai, T. (2017). Artificial intelligence in precision cardiovascular medicine. Journal of the American College of Cardiology, 69(21), 2657–2664. https://doi.org/10.1016/j.jacc.2017.03.571
  • 6. Brynjolfsson, E., & McAfee, A. (2019). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. New York, NY: W.W. Norton & Company.
  • 7. Ehteshami Bejnordi, B., Veta, M., Van Diest, P. J., Van Ginneken, B., Karssemeijer, N., Litjens, G., … Venâncio, R. (2017). Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA, 318(22), 2199–2210. https://doi.org/10.1001/jama.2017.14585
  • 8. Guo, J., & Li, B. (2018). The application of medical artificial intelligence technology in rural areas of developing countries. Health Equity, 2(1), 174–181. https://doi.org/10.1089/heq.2018.0037
  • 9. T.C. Sağlık Bakanlığı Sağlık Bilgi Sistemleri Genel Müdürlüğü. (2024). FİTAS (Filyasyon ve İzolasyon Takip Sistemi). Retrieved May 1, 2024, from https://sbsgm.saglik.gov.tr/TR,73584/fitas.html
  • 10. Sallstrom, L., Morris, O., & Mehta, H. (2019). Ethical considerations: Artificial intelligence in Africa’s healthcare. ORF Issue Brief, 312. Retrieved from https://www.orfonline.org/wp-content/uploads/2019/09/ORF_Issue_Brief_312_AI-Health-Africa.pdf
  • 11. T.C. Sağlık Bakanlığı. (2020). Hayat Eve Sığar Uygulaması. Retrieved from https://www.saglik.gov.tr
  • 12. Balyen, L., & Peto, T. (2019). Promising artificial intelligence–machine learning–deep learning algorithms in ophthalmology. Asia-Pacific Journal of Ophthalmology, 8(3), 264–272. https://doi.org/10.22608/APO.2018479
  • 13. Teare, P., Fishman, M., Benzaquen, O., Toledano, E., & Elnekave, E. (2017). Malignancy detection on mammography using dual deep convolutional neural networks and genetically discovered false color input enhancement. Journal of Digital Imaging, 30(4), 499–505. https://doi.org/10.1007/s10278-017-9992-3
  • 14. Curioni-Fontecedro, A. (2017). A new era of oncology through artificial intelligence. ESMO Open, 2(2), Article e000198. https://doi.org/10.1136/esmoopen-2017-000198
  • 15. Larson, D. B., Chen, M. C., Lungren, M. P., Halabi, S. S., Stence, N. V., & Langlotz, C. P. (2018). Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs. Radiology, 287(1), 313–322. https://doi.org/10.1148/radiol.2017170236
  • 16. Griffin, J., & Treanor, D. (2017). Digital pathology in clinical use: Where are we now and what is holding us back? Histopathology, 70(1), 134–145. https://doi.org/10.1111/his.12993
  • 17. Kalis, B., Collier, M., & Fu, R. (2018). 10 promising AI applications in health care. Harvard Business Review. Retrieved from https://hbr.org/2018/05/10-promising-ai-applications-in-health-care
  • 18. Haug, C. J., & Drazen, J. M. (2023). Artificial intelligence and machine learning in clinical medicine, 2023. New England Journal of Medicine, 388(13), 1201–1208. https://doi.org/10.1056/NEJMra2302038
  • 19. Erden, F., Velipasalar, S., Alkar, A. Z., & Cetin, A. E. (2016). Sensors in assisted living: A survey of signal and image processing methods. IEEE Signal Processing Magazine, 33(2), 36–44. https://doi.org/10.1109/MSP.2015.2489978
  • 20. Hamrock, E., Paige, K., Parks, J., Scheulen, J., & Levin, S. (2013). Discrete event simulation for healthcare organizations: A tool for decision making. Journal of Healthcare Management, 58(2), 110–124. https://doi.org/10.1097/00115514-201303000-00006
  • 21. Curtis, R. G., Bartel, B., Ferguson, T., Blake, H. T., Northcott, C., Virgara, R., & Maher, C. A. (2021). Improving user experience of virtual health assistants: A scoping review. Journal of Medical Internet Research, 23(12), Article e31737. https://doi.org/10.2196/31737
  • 22. Gravenhorst, F., Muaremi, A., Bardram, J., Frost, M., Tuxen, A., Lukowicz, P., & Tröster, G. (2015). Mobile phones as medical devices in mental disorder treatment: An overview. Personal and Ubiquitous Computing, 19(2), 335–353. https://doi.org/10.1007/s00779-014-0829-5
  • 23. Kumar, P. S., & Suresh, S. (2019). Artificial intelligence in healthcare: A review. International Journal of Advanced Research in Computer Science, 10(3), 12–18.
  • 24. Xue, Y., Zhang, R., Deng, Y., Chen, K., & Jiang, T. (2017). A preliminary examination of the diagnostic value of deep learning in hip osteoarthritis. PLoS ONE, 12(6), e0178992. https://doi.org/10.1371/journal.pone.0178992
  • 25. Panch, T., Szolovits, P., & Atun, R. (2018). Artificial intelligence, machine learning and health systems. Journal of Global Health, 8(2), 020303. https://doi.org/10.7189/jogh.08.020303
  • 26. Wan, T. T. H. (2018). Reducing readmissions of patients with chronic conditions: Designing a clinical decision support system for care management interventions. In Population health management in chronic conditions (pp. 165–178). Cham, Switzerland: Springer International Publishing. https://doi.org/10.1007/978-3-319-68756-8_8
  • 27. Gandhi, S. O., & Sabik, L. (2014). Emergency department visit classification using the NYU algorithm. The American Journal of Managed Care, 20(4), 315–320. PMID: 24884862
  • 28. Madabhushi, A., & Lee, G. (2016). Image analysis and machine learning in digital pathology: Challenges and opportunities. Medical Image Analysis, 33, 170–175. https://doi.org/10.1016/j.media.2016.06.037
  • 29. Al, U., & Coştur, R. (2007). Türk Psikoloji Dergisi’nin bibliyometrik profili. Türk Kütüphaneciliği, 21(2), 142–163. 30. Üstdiken, B., & Pasadeos, Y. (1993). Türkiye’de örgütler ve yönetim yazını. Amme İdaresi Dergisi, 26(2), 73–93.
  • 31. Diodato, V. P. (1994). Dictionary of bibliometrics. Portland, OR: The Hawthorne Press.
  • 32. Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629
  • 33. McBurney, M. K., & Novak, P. L. (2002). What is bibliometrics and why should you care? In Professional Communication Conference, Portland, OR, USA, 108-114. https://doi.org/10.1109/IPCC.2002.1049094
  • 34. Krauskopf, E. (2018). A bibliometric analysis of the Journal of Infection and Public Health: 2008–2016. Journal of Infection and Public Health, 11(2), 224–229. https://doi.org/10.1016/j.jiph.2017.12.002
  • 35. Meskó, B., Drobni, Z., Bényei, E., Gergely, B., & Győrffy, Z. (2017). Digital health is a cultural transformation of traditional healthcare. mHealth, 3, Article 38. https://doi.org/10.21037/mhealth.2017.08.07
  • 36. Buch, V. H., Ahmed, I., & Maruthappu, M. (2018). Artificial intelligence in medicine: Current trends and future possibilities. British Journal of General Practice, 68(668), 143–144. https://doi.org/10.3399/bjgp18X695213
  • 37. 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), 230–243. https://doi.org/10.1136/svn-2017-000101

SAĞLIK HİZMETLERİNDE YAPAY ZEKA KULLANIMININ BİBLİYOMETRİK ANALİZİ

Year 2025, Volume: 5 Issue: 2, 11 - 20, 29.08.2025

Abstract

Amaç: Bu çalışma, Türkiye’de sağlık hizmetlerinde yapay zekâ (AI) kullanımına dair 1995-2024 yılları arasında Scopus veri tabanından elde edilen 487 kaynağı inceleyerek bibliyometrik bir analiz sunmayı amaçlamaktadır. Literatürdeki etkili yayınları, yazarları, kurumları ve anahtar kelimeleri belirleyerek alandaki trendleri ve gelecekteki araştırma yönelimlerini değerlendirmek, Türkiye’nin sağlık araştırma ekosistemine katkı sağlamayı hedefler.
Yöntem: Scopus’ta “artificial intelligence” ve “health services” anahtar kelimeleriyle yapılan aramada 31.668 sonuç elde edilmiş, Türkiye ile sınırlandırılarak 487 kaynağa indirgenmiştir. Bu kaynaklar (316 makale, 61 kitap bölümü, 57 kabul edilmiş makale, 39 bildiri, 7 editoryal yazı, 4 belge, 3 kitap) Woswiver programı ile analiz edilmiştir. Yazar, atıf, anahtar kelime, dergi ve kurum verileri üzerinden bibliyometrik analiz gerçekleştirilmiştir.
Bulgular: Türkiye’de AI odaklı sağlık araştırmaları, özellikle 2023’te (134 yayın) son 10 yılda yoğunlaşmıştır. Makaleler (%64,9) yayın türlerinde öne çıkarken, Pamucar (6 yayın) ve Kahraman, Yüksel (5’er yayın) en üretken yazarlardır. Hacettepe Üniversitesi (29 yayın) lider kurumdur. Bilgisayar bilimi (%21) ve tıp (%16,9) başlıca çalışma alanlarıdır. AI, teşhis, tedavi, koruyucu sağlık ve yönetimde etkili olup, Covid-19 pandemisiyle kullanım artmıştır.
Sonuç: AI, Türkiye’de sağlık hizmetlerinde dönüştürücü bir rol oynamaktadır. Ancak, altyapı eksiklikleri, veri güvenliği ve etik sorunlar gibi zorluklar devam etmektedir. Gelecekte, altyapı yatırımları, etik düzenlemeler ve disiplinlerarası çalışmalarla AI’nin sağlık hizmetlerindeki potansiyeli sürdürülebilir ve eşitlikçi bir şekilde artırılabilir.

References

  • 1. Hamet, P., & Tremblay, J. (2017). Artificial intelligence in medicine. Metabolism, 69(Suppl.), S36–S40. https://doi.org/10.1016/j.metabol.2017.01.011
  • 2. Secinaro, S., Calandra, D., Secinaro, A., Muthurangu,v., & Biancone, P. (2021). The role of artificial intelligence in healthcare: A structured literature review. BMC Medical Informatics and Decision Making, 21(1), Article 125. https://doi.org/10.1186/s12911-021-01488-9
  • 3. World Health Organization. (2000). The world health report 2000: Health systems: Improving performance. Geneva, Switzerland: WHO.
  • 4. Donabedian, A. (1988). The quality of care: How can it be assessed? Journal of the American Medical Association, 260(12), 1743–1748. https://doi.org/10.1001/jama.1988.03410120089033
  • 5. Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., & Kitai, T. (2017). Artificial intelligence in precision cardiovascular medicine. Journal of the American College of Cardiology, 69(21), 2657–2664. https://doi.org/10.1016/j.jacc.2017.03.571
  • 6. Brynjolfsson, E., & McAfee, A. (2019). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. New York, NY: W.W. Norton & Company.
  • 7. Ehteshami Bejnordi, B., Veta, M., Van Diest, P. J., Van Ginneken, B., Karssemeijer, N., Litjens, G., … Venâncio, R. (2017). Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA, 318(22), 2199–2210. https://doi.org/10.1001/jama.2017.14585
  • 8. Guo, J., & Li, B. (2018). The application of medical artificial intelligence technology in rural areas of developing countries. Health Equity, 2(1), 174–181. https://doi.org/10.1089/heq.2018.0037
  • 9. T.C. Sağlık Bakanlığı Sağlık Bilgi Sistemleri Genel Müdürlüğü. (2024). FİTAS (Filyasyon ve İzolasyon Takip Sistemi). Retrieved May 1, 2024, from https://sbsgm.saglik.gov.tr/TR,73584/fitas.html
  • 10. Sallstrom, L., Morris, O., & Mehta, H. (2019). Ethical considerations: Artificial intelligence in Africa’s healthcare. ORF Issue Brief, 312. Retrieved from https://www.orfonline.org/wp-content/uploads/2019/09/ORF_Issue_Brief_312_AI-Health-Africa.pdf
  • 11. T.C. Sağlık Bakanlığı. (2020). Hayat Eve Sığar Uygulaması. Retrieved from https://www.saglik.gov.tr
  • 12. Balyen, L., & Peto, T. (2019). Promising artificial intelligence–machine learning–deep learning algorithms in ophthalmology. Asia-Pacific Journal of Ophthalmology, 8(3), 264–272. https://doi.org/10.22608/APO.2018479
  • 13. Teare, P., Fishman, M., Benzaquen, O., Toledano, E., & Elnekave, E. (2017). Malignancy detection on mammography using dual deep convolutional neural networks and genetically discovered false color input enhancement. Journal of Digital Imaging, 30(4), 499–505. https://doi.org/10.1007/s10278-017-9992-3
  • 14. Curioni-Fontecedro, A. (2017). A new era of oncology through artificial intelligence. ESMO Open, 2(2), Article e000198. https://doi.org/10.1136/esmoopen-2017-000198
  • 15. Larson, D. B., Chen, M. C., Lungren, M. P., Halabi, S. S., Stence, N. V., & Langlotz, C. P. (2018). Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs. Radiology, 287(1), 313–322. https://doi.org/10.1148/radiol.2017170236
  • 16. Griffin, J., & Treanor, D. (2017). Digital pathology in clinical use: Where are we now and what is holding us back? Histopathology, 70(1), 134–145. https://doi.org/10.1111/his.12993
  • 17. Kalis, B., Collier, M., & Fu, R. (2018). 10 promising AI applications in health care. Harvard Business Review. Retrieved from https://hbr.org/2018/05/10-promising-ai-applications-in-health-care
  • 18. Haug, C. J., & Drazen, J. M. (2023). Artificial intelligence and machine learning in clinical medicine, 2023. New England Journal of Medicine, 388(13), 1201–1208. https://doi.org/10.1056/NEJMra2302038
  • 19. Erden, F., Velipasalar, S., Alkar, A. Z., & Cetin, A. E. (2016). Sensors in assisted living: A survey of signal and image processing methods. IEEE Signal Processing Magazine, 33(2), 36–44. https://doi.org/10.1109/MSP.2015.2489978
  • 20. Hamrock, E., Paige, K., Parks, J., Scheulen, J., & Levin, S. (2013). Discrete event simulation for healthcare organizations: A tool for decision making. Journal of Healthcare Management, 58(2), 110–124. https://doi.org/10.1097/00115514-201303000-00006
  • 21. Curtis, R. G., Bartel, B., Ferguson, T., Blake, H. T., Northcott, C., Virgara, R., & Maher, C. A. (2021). Improving user experience of virtual health assistants: A scoping review. Journal of Medical Internet Research, 23(12), Article e31737. https://doi.org/10.2196/31737
  • 22. Gravenhorst, F., Muaremi, A., Bardram, J., Frost, M., Tuxen, A., Lukowicz, P., & Tröster, G. (2015). Mobile phones as medical devices in mental disorder treatment: An overview. Personal and Ubiquitous Computing, 19(2), 335–353. https://doi.org/10.1007/s00779-014-0829-5
  • 23. Kumar, P. S., & Suresh, S. (2019). Artificial intelligence in healthcare: A review. International Journal of Advanced Research in Computer Science, 10(3), 12–18.
  • 24. Xue, Y., Zhang, R., Deng, Y., Chen, K., & Jiang, T. (2017). A preliminary examination of the diagnostic value of deep learning in hip osteoarthritis. PLoS ONE, 12(6), e0178992. https://doi.org/10.1371/journal.pone.0178992
  • 25. Panch, T., Szolovits, P., & Atun, R. (2018). Artificial intelligence, machine learning and health systems. Journal of Global Health, 8(2), 020303. https://doi.org/10.7189/jogh.08.020303
  • 26. Wan, T. T. H. (2018). Reducing readmissions of patients with chronic conditions: Designing a clinical decision support system for care management interventions. In Population health management in chronic conditions (pp. 165–178). Cham, Switzerland: Springer International Publishing. https://doi.org/10.1007/978-3-319-68756-8_8
  • 27. Gandhi, S. O., & Sabik, L. (2014). Emergency department visit classification using the NYU algorithm. The American Journal of Managed Care, 20(4), 315–320. PMID: 24884862
  • 28. Madabhushi, A., & Lee, G. (2016). Image analysis and machine learning in digital pathology: Challenges and opportunities. Medical Image Analysis, 33, 170–175. https://doi.org/10.1016/j.media.2016.06.037
  • 29. Al, U., & Coştur, R. (2007). Türk Psikoloji Dergisi’nin bibliyometrik profili. Türk Kütüphaneciliği, 21(2), 142–163. 30. Üstdiken, B., & Pasadeos, Y. (1993). Türkiye’de örgütler ve yönetim yazını. Amme İdaresi Dergisi, 26(2), 73–93.
  • 31. Diodato, V. P. (1994). Dictionary of bibliometrics. Portland, OR: The Hawthorne Press.
  • 32. Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629
  • 33. McBurney, M. K., & Novak, P. L. (2002). What is bibliometrics and why should you care? In Professional Communication Conference, Portland, OR, USA, 108-114. https://doi.org/10.1109/IPCC.2002.1049094
  • 34. Krauskopf, E. (2018). A bibliometric analysis of the Journal of Infection and Public Health: 2008–2016. Journal of Infection and Public Health, 11(2), 224–229. https://doi.org/10.1016/j.jiph.2017.12.002
  • 35. Meskó, B., Drobni, Z., Bényei, E., Gergely, B., & Győrffy, Z. (2017). Digital health is a cultural transformation of traditional healthcare. mHealth, 3, Article 38. https://doi.org/10.21037/mhealth.2017.08.07
  • 36. Buch, V. H., Ahmed, I., & Maruthappu, M. (2018). Artificial intelligence in medicine: Current trends and future possibilities. British Journal of General Practice, 68(668), 143–144. https://doi.org/10.3399/bjgp18X695213
  • 37. 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), 230–243. https://doi.org/10.1136/svn-2017-000101
There are 36 citations in total.

Details

Primary Language Turkish
Subjects Computing Applications in Health, Intelligent Robotics, Knowledge Representation and Reasoning, Fuzzy Computation, Natural Language Processing, Health Informatics and Information Systems
Journal Section Research Article
Authors

Kader Gerçeker 0000-0002-9908-3488

Ramazan Erdem 0000-0001-6951-3814

Publication Date August 29, 2025
Submission Date June 14, 2025
Acceptance Date August 7, 2025
Published in Issue Year 2025 Volume: 5 Issue: 2

Cite

Vancouver Gerçeker K, Erdem R. SAĞLIK HİZMETLERİNDE YAPAY ZEKA KULLANIMININ BİBLİYOMETRİK ANALİZİ. JAIHS. 2025;5(2):11-20.