Derleme
BibTex RIS Kaynak Göster

Üretken Yapay Zeka: Sağlıkta Hizmetlerinde Kullanımı, Üstün ve Zayıf Yanları

Yıl 2025, Cilt: 9 Sayı: 2, 51 - 61, 31.08.2025
https://doi.org/10.34084/bshr.1731738

Öz

Büyük dil modelleri (LLM) ve üretken yapay zeka (GenAI) sistemleri, sağlık hizmetlerinin sunum şeklini, tıbbi araştırmaların yürütülmesini ve hastaların bilgiyle etkileşim kurma biçimlerini dönüştürmeye başlamıştır. Bu modeller, genellikle erken tanı, yeni ilaçların geliştirilmesi, hasta eğitimi ve katılımı, hastalık salgını erken uyarı sistemleri, bireyselleştirilmiş tıp, genom madenciliği gibi alanlarda kullanılmakta ve pek çok alan için gelecek vaat etmektedir.
Yapay zeka sistemlerinin sağlık hizmetlerini dönüştürücü potansiyelinin yanı sıra bir dizi etik sorunları, yönetsel ve operasyonel zorlukları da bulunmaktadır. Üretken AI'nın sağlık hizmetlerinde kullanımı sırasında mahremiyet tartışmaları, algoritmik yanlılıklar ve hasta güvenliği gibi sorunlar söz konusu olabildiğinden geliştiriciler, klinisyenler, politika yapıcılar ve kurumlara önemli sorumluluklar düşmektedir.
Bu yazıda önce konuya ilişkin temel kavramlar açıklanmış, sonra tıpta ve bilimsel çalışmalarda yapay zeka kullanımına ilişkin mevcut durum değerlendirmesi yapılarak fırsatlar, olası riskler, geleceğe yönelik öngörüler ve öneriler özetlenmiştir.

Etik Beyan

-

Destekleyen Kurum

-

Teşekkür

-

Kaynakça

  • 1. McKinsey&Company. Generative AI in healthcare: Current trends and future Outlook. Collaborative Report, 2025 https://www.mckinsey.com/industries/healthcare/our-insights/generative-ai-in-healthcare-current-trends-and-future-outlook#/ Erişim:25.Haziran.2025.
  • 2. Topol E. Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books, 2019. 3. Shen J, Zhang CJP, Jiang B, et al. Artificial intelligence versus clinicians in disease diagnosis: Systematic review. JMIR Medical Informatics, 2023;11, e43230.
  • 4. National Academy of Medicine. Generative Artificial Intelligence in Health and Medicine: Opportunities and Responsibilities for Transformative Innovation. Washington, DC: The National Academies Press, 2025. https://doi.org/10.17226/28907.
  • 5. Duggan MJ, Gervase J, Schoenbaum A, et al. Clinician experiences with ambient scribe technology to assist with documentation burden and efficiency. JAMA Netw Open 2025; 8(2):e2460637.)
  • 6. Tierney AA, Gayre G, Hoberman B, et al. Ambient artificial intelligence scribes to alleviate the burden of clinical documentation. NEJM Catal Innov Care Deliv 2024; 5(3) (https://catalyst.nejm.org/doi/full/10.1056/CAT.23.0404).
  • 7. Gandhi TK, Classen D, Sinsky CA, et al. How can artificial intelligence decrease cognitive and work burden for front line practitioners? JAMIA Open 2023; 6(3): ooad079.
  • 8. Zhavoronkov A, Ivanenkov YA, Aliper A, et al. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nature Biotechnology, 2019;37(9):1038–1040.
  • 9. Niraula D, Cuneo KC, Dinov ID, et al. Intricacies of human–AI interaction in dynamic decision-making for precision oncology. Nat Commun 2025; 16: 1138.
  • 10. Goodman RS, Patrinely JR, Stone CA, et al. Accuracy and Reliability of Chatbot Responses to Physician Questions. JAMA Netw Open. 2023;6(10):e2336483. doi:10.1001/jamanetworkopen.2023.36483.
  • 11. Kanjee Z, Crowe B, Rodman A. Accuracy of a Generative Artificial Intelligence Model in a Complex Diagnostic Challenge. JAMA. 2023;330(1):78–80. doi:10.1001/jama.2023.8288.
  • 12. Bickmore TW, Trinh H, Olafsson S, et al. Patient and consumer safety risks when using conversational assistants for medical information: An observational study of Siri, Alexa, and Google Assistant. JAMA Network Open, 2022;5(7): e2222204.
  • 13. S.A.R.A.H. (https://www.who.int/campaigns/s-a-r-a-h) Erişim: 25. Haziran.2025.
  • 14. Florence (https://www.who.int/news/item/04-10-2022-who-and-partners-launch-world-s-most-extensive-freely-accessible-ai-health-worker) Erişim: 25. Haziran.2025.
  • 15. HealthBuddy+ (https://www.who.int/news-room/feature-stories/detail/scicom-compilation-healthbuddy) Erişim: 25. Haziran.2025.
  • 16. Aggarwal A, Tam CC, Wu D, et al. Artificial intelligence-based chatbots for promoting health behavioral changes: systematic review. J Med Internet Res 2023; 25: e40789.
  • 17. Yang X, Xiao Y, Liu D, et al. Enhancing doctor-patient communication using large language models for pathology report interpretation. BMC Med Inform Decis Mak 2025; 25: 36.
  • 18. Rao P, McGee LM, Seideman CA. A comparative assessment of ChatGPT vs. Google Translate for the translation of patient instructions. JMAI 2024; 7. (https://jmai.amegroups.org/article/view/9019/html).
  • 19. Zaretsky J, Kim JM, Baskharoun S, et al. Generative artificial intelligence to transform inpatient discharge summaries to patient-friendly language and format. JAMA Netw Open 2024; 7(3):e240357.
  • 20. Hernandez M, Epelde G, Alberdi A, et al. Synthetic data generation for tabular health records: A systematic review, Neurocomputing 2022;493: 28–45, https://doi.org/10.1016/J.NEUCOM.2022.04.053.
  • 21. Rujas M, Martín Gómez Del Moral Herranz R, et al. Synthetic data generation in healthcare: A scoping review of reviews on domains, motivations, and future applications. Int J Med Inform. 2025;195:105763. (https://doi.org/10.1016/j.neucom.2022.04.053.
  • 22. Mao X, Huang Y, Jin Y, et al. A phenotype-based AI pipeline outperforms human experts in differentially diagnosing rare diseases using EHRs. NPJ Digit Med 2025; 8: 68.) (Gangwal A, Lavecchia A. AI-driven drug discovery for rare diseases. J Chem Inf Model 2025; 65: 2214-31.
  • 23. Topol EJ. Learning the language of life with AI. Science 2025; 387(6733): eadv4414.
  • 24. Kraemer MUG, Tsui JL-H, Chang SY, et al. Artificial intelligence for modelling infectious disease epidemics. Nature 2025; 638: 623-35.
  • 25. Omar M, Soffer S, Agbareia R. et al. Sociodemographic biases in medical decision making by large language models. Nat Med, 2025;31:1873–1881. https://doi.org/10.1038/s41591-025-03626-6.
  • 26. Farquhar S, Kossen J, Kuhn L, Gal Y. Detecting hallucinations in large language models using semantic entropy. Nature 2024;630:625–630. https://doi.org/10.1038/s41586-024-07421-0.
  • 27. EU. AI Act. (https://artificialintelligenceact.eu/the-act/) Erişim: 29.Haziran.2025.
  • 28. FDA. Software as a Medical Device-SaMD. (https://www.fda.gov/medical-devices/digital-health-center-excellence/software-medical-device-samd) Erişim: 29.Haziran.2025.

Generative Artificial Intelligence: Its Use in Healthcare Services, Strengths and Weaknesses

Yıl 2025, Cilt: 9 Sayı: 2, 51 - 61, 31.08.2025
https://doi.org/10.34084/bshr.1731738

Öz

Large language models (LLMs) and generative artificial intelligence (GenAI) systems are beginning to transform the way healthcare is delivered, medical research is conducted, and patients’ interaction with medical information.
These models are currently being used in areas such as early diagnosis, the development of new drugs, patient education and engagement, disease outbreak early warning systems, personalized medicine, and genomic mining, and hold great promise for many other fields. In addition to their transformative potential, AI systems in healthcare also present a range of ethical issues, managerial, and operational challenges. The use of generative AI in healthcare raises concerns such as privacy debates, algorithmic biases, and patient safety, thereby imposing significant responsibilities on developers, clinicians, policymakers, and institutions.
This article first explains the basic concepts related to the topic, then provides an assessment of the current state of AI use in medicine and scientific research, and finally summarizes the opportunities, potential risks, future projections, and recommendations.

Etik Beyan

-

Destekleyen Kurum

-

Teşekkür

-

Kaynakça

  • 1. McKinsey&Company. Generative AI in healthcare: Current trends and future Outlook. Collaborative Report, 2025 https://www.mckinsey.com/industries/healthcare/our-insights/generative-ai-in-healthcare-current-trends-and-future-outlook#/ Erişim:25.Haziran.2025.
  • 2. Topol E. Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books, 2019. 3. Shen J, Zhang CJP, Jiang B, et al. Artificial intelligence versus clinicians in disease diagnosis: Systematic review. JMIR Medical Informatics, 2023;11, e43230.
  • 4. National Academy of Medicine. Generative Artificial Intelligence in Health and Medicine: Opportunities and Responsibilities for Transformative Innovation. Washington, DC: The National Academies Press, 2025. https://doi.org/10.17226/28907.
  • 5. Duggan MJ, Gervase J, Schoenbaum A, et al. Clinician experiences with ambient scribe technology to assist with documentation burden and efficiency. JAMA Netw Open 2025; 8(2):e2460637.)
  • 6. Tierney AA, Gayre G, Hoberman B, et al. Ambient artificial intelligence scribes to alleviate the burden of clinical documentation. NEJM Catal Innov Care Deliv 2024; 5(3) (https://catalyst.nejm.org/doi/full/10.1056/CAT.23.0404).
  • 7. Gandhi TK, Classen D, Sinsky CA, et al. How can artificial intelligence decrease cognitive and work burden for front line practitioners? JAMIA Open 2023; 6(3): ooad079.
  • 8. Zhavoronkov A, Ivanenkov YA, Aliper A, et al. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nature Biotechnology, 2019;37(9):1038–1040.
  • 9. Niraula D, Cuneo KC, Dinov ID, et al. Intricacies of human–AI interaction in dynamic decision-making for precision oncology. Nat Commun 2025; 16: 1138.
  • 10. Goodman RS, Patrinely JR, Stone CA, et al. Accuracy and Reliability of Chatbot Responses to Physician Questions. JAMA Netw Open. 2023;6(10):e2336483. doi:10.1001/jamanetworkopen.2023.36483.
  • 11. Kanjee Z, Crowe B, Rodman A. Accuracy of a Generative Artificial Intelligence Model in a Complex Diagnostic Challenge. JAMA. 2023;330(1):78–80. doi:10.1001/jama.2023.8288.
  • 12. Bickmore TW, Trinh H, Olafsson S, et al. Patient and consumer safety risks when using conversational assistants for medical information: An observational study of Siri, Alexa, and Google Assistant. JAMA Network Open, 2022;5(7): e2222204.
  • 13. S.A.R.A.H. (https://www.who.int/campaigns/s-a-r-a-h) Erişim: 25. Haziran.2025.
  • 14. Florence (https://www.who.int/news/item/04-10-2022-who-and-partners-launch-world-s-most-extensive-freely-accessible-ai-health-worker) Erişim: 25. Haziran.2025.
  • 15. HealthBuddy+ (https://www.who.int/news-room/feature-stories/detail/scicom-compilation-healthbuddy) Erişim: 25. Haziran.2025.
  • 16. Aggarwal A, Tam CC, Wu D, et al. Artificial intelligence-based chatbots for promoting health behavioral changes: systematic review. J Med Internet Res 2023; 25: e40789.
  • 17. Yang X, Xiao Y, Liu D, et al. Enhancing doctor-patient communication using large language models for pathology report interpretation. BMC Med Inform Decis Mak 2025; 25: 36.
  • 18. Rao P, McGee LM, Seideman CA. A comparative assessment of ChatGPT vs. Google Translate for the translation of patient instructions. JMAI 2024; 7. (https://jmai.amegroups.org/article/view/9019/html).
  • 19. Zaretsky J, Kim JM, Baskharoun S, et al. Generative artificial intelligence to transform inpatient discharge summaries to patient-friendly language and format. JAMA Netw Open 2024; 7(3):e240357.
  • 20. Hernandez M, Epelde G, Alberdi A, et al. Synthetic data generation for tabular health records: A systematic review, Neurocomputing 2022;493: 28–45, https://doi.org/10.1016/J.NEUCOM.2022.04.053.
  • 21. Rujas M, Martín Gómez Del Moral Herranz R, et al. Synthetic data generation in healthcare: A scoping review of reviews on domains, motivations, and future applications. Int J Med Inform. 2025;195:105763. (https://doi.org/10.1016/j.neucom.2022.04.053.
  • 22. Mao X, Huang Y, Jin Y, et al. A phenotype-based AI pipeline outperforms human experts in differentially diagnosing rare diseases using EHRs. NPJ Digit Med 2025; 8: 68.) (Gangwal A, Lavecchia A. AI-driven drug discovery for rare diseases. J Chem Inf Model 2025; 65: 2214-31.
  • 23. Topol EJ. Learning the language of life with AI. Science 2025; 387(6733): eadv4414.
  • 24. Kraemer MUG, Tsui JL-H, Chang SY, et al. Artificial intelligence for modelling infectious disease epidemics. Nature 2025; 638: 623-35.
  • 25. Omar M, Soffer S, Agbareia R. et al. Sociodemographic biases in medical decision making by large language models. Nat Med, 2025;31:1873–1881. https://doi.org/10.1038/s41591-025-03626-6.
  • 26. Farquhar S, Kossen J, Kuhn L, Gal Y. Detecting hallucinations in large language models using semantic entropy. Nature 2024;630:625–630. https://doi.org/10.1038/s41586-024-07421-0.
  • 27. EU. AI Act. (https://artificialintelligenceact.eu/the-act/) Erişim: 29.Haziran.2025.
  • 28. FDA. Software as a Medical Device-SaMD. (https://www.fda.gov/medical-devices/digital-health-center-excellence/software-medical-device-samd) Erişim: 29.Haziran.2025.
Toplam 27 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Bilgi Temsili ve Akıl Yürütme
Bölüm Derleme
Yazarlar

Osman Hayran 0000-0002-9994-5033

Erken Görünüm Tarihi 10 Eylül 2025
Yayımlanma Tarihi 31 Ağustos 2025
Gönderilme Tarihi 1 Temmuz 2025
Kabul Tarihi 5 Temmuz 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 9 Sayı: 2

Kaynak Göster

AMA Hayran O. Üretken Yapay Zeka: Sağlıkta Hizmetlerinde Kullanımı, Üstün ve Zayıf Yanları. J Biotechnol and Strategic Health Res. Ağustos 2025;9(2):51-61. doi:10.34084/bshr.1731738
  • Dergimiz Uluslararası hakemli bir dergi olup TÜRKİYE ATIF DİZİNİ, TürkMedline, CrossREF, ASOS index, Google Scholar, JournalTOCs, Eurasian Scientific Journal Index(ESJI), SOBIAD ve ISIindexing dizinlerinde taranmaktadır. TR Dizin(ULAKBİM), SCOPUS, DOAJ için başvurularımızın sonuçlanması beklenmektedir.