Artificial Intelligence and Healthcare Applications

Öz

Artificial intelligence (AI) has increasingly become integrated into healthcare systems in recent years, particularly in areas such as diagnosis, treatment planning, clinical decision support systems, medical imaging, drug discovery and patient management. Advances in machine learning, deep learning, generative AI and multimodal AI systems are transforming clinical practice across many medical specialties, including radiology, internal medicine, surgical sciences and respiratory medicine. AI-assisted systems have the potential to reduce workload, contribute to early diagnosis, support personalized treatment approaches and improve health economics. However, important concerns regarding data security, algorithmic bias, clinical validation, ethical responsibility and regulatory frameworks remain under discussion. In this review, current applications of artificial intelligence in healthcare—including its use in internal and surgical sciences, radiological applications, health economic contributions, and ethical considerations—are evaluated in light of the current literature, emphasizing the need for clinical validation, human oversight, and ethical principles for the safe and sustainable integration of AI technologies into healthcare systems.

Anahtar Kelimeler

• Artificial intelligence
• machine learning
• healthcare assistants

Destekleyen Kurum

No institutional support was received.

Etik Beyan

The Helsinki Declaration, published by the World Medical Association (WMA), adheres to fundamental ethical guidelines that all medical and scientific research involving human participants must follow.

Yapay Zeka ve Sağlık Uygulamaları

Öz

Yapay zekâ (YZ), son yıllarda sağlık hizmetlerinde tanı, tedavi planlaması, klinik karar destek sistemleri, tıbbi görüntüleme, ilaç geliştirme ve hasta yönetimi gibi birçok alanda giderek daha yaygın kullanılmaya başlanmıştır. Makine öğrenmesi, derin öğrenme, üretken yapay zekâ ve multimodal yapay zekâ sistemlerindeki gelişmeler; radyoloji, dahili bilimler, cerrahi bilimler ve solunum hastalıkları başta olmak üzere birçok tıbbi branşta klinik uygulamaları dönüştürmektedir. Yapay zekâ destekli sistemlerin iş yükünü azaltma, erken tanıya katkı sağlama, kişiselleştirilmiş tedavi yaklaşımlarını destekleme ve sağlık ekonomisine katkıda bulunma potansiyeli bulunmaktadır. Bununla birlikte veri güvenliği, algoritmik yanlılık, klinik validasyon, etik sorumluluk ve yasal düzenlemeler gibi önemli sorunlar halen tartışılmaktadır. Bu derlemede yapay zekânın sağlık alanındaki güncel uygulamaları; dahili ve cerrahi bilimlerdeki kullanımları, radyolojik uygulamalar, sağlık ekonomisine katkıları ve etik boyutları güncel literatür ışığında değerlendirilmiş; sağlık sistemlerine güvenli ve sürdürülebilir entegrasyonu için klinik doğrulama, insan denetimi ve etik ilkelerin gerekliliği vurgulanmıştır.

Anahtar Kelimeler

• Yapay zeka
• makine öğrenmesi
• sağlık asistanları

Destekleyen Kurum

Kurumsal bir destek alınmamıştır.

Etik Beyan

Dünya Tıp Birliği (WMA) tarafından yayınlanan Helsinki Deklarasyonu, insan katılımcılarla yapılan tüm tıbbi ve bilimsel araştırmaların uyması gereken temel etik kurallara bağlı kalmaktadır.

Kaynakça

1. 1. Walsh T, Levy N, Bell G, et al. The Effective and Ethical Development of Artificial Intelligence: An Opportunity to Improve Our Wellbeing. Australian Council of Learned Academies (ACOLA); 2019. Accessed May 1, 2026. https://researchers.mq.edu.au/en/publications/the-effective-and-ethical-development-of-artificial-intelligence-/
2. 2. Turing test | The Alan Turing Institute. Accessed April 28, 2026. https://www.turing.ac.uk/taxonomy/term/1242
3. 3. Arf C. Makine Düşünebilir mi ve Nasıl Düşünebilir? Atatürk Üniversitesi 1958-1959 Öğretim Yılı Halk Konferansları 1959;(1):91-103.
4. 4. Tran BX, Vu GT, Ha GH, et al. Global Evolution of Research in Artificial Intelligence in Health and Medicine: A Bibliometric Study. J Clin Med. 2019;8(3):360.
5. 5. Garcia Alonso R, Thoene U, Davila Benavides D. Digital Health and Artificial Intelligence: Advancing Healthcare Provision in Latin America. IT Prof. 2022;24(2):62-68.
6. 6. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56.
7. 7. Mesko B. Tıbbın Geleceğine Yolculuk. İstanbul: Optimist Yayın Grubu, 2018; 174-183.
8. 8. Somashekhar SP, Sepúlveda MJ, Puglielli S, et al. Watson for Oncology and breast cancer treatment recommendations: agreement with an expert multidisciplinary tumor board. Ann Oncol. 2018;29(2):418-423.

9. 9. Birinci Ş. A Digital Opportunity for Patients to Manage Their Health: Turkey National Personal Health Record System (The e-Nabız). Balkan Med J. 2023;40(3):215-221.
10. 10. Liu G, Catacutan DB, Rathod K, et al. Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii. Nat Chem Biol. 2023;19(11):1342-1350.
11. 11. Fahrner LJ, Chen E, Topol E, Rajpurkar P. The generative era of medical AI. Cell. 2025;188(14):3648-3660.
12. 12. Stokes JM, Yang K, Swanson K, et al. A Deep Learning Approach to Antibiotic Discovery. Cell. 2020;180(4):688-702.e13.
13. 13. Attia ZI, Noseworthy PA, Lopez-Jimenez F, et al. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. The Lancet. 2019;394(10201):861-867.
14. 14. Abràmoff MD, Lavin PT, Birch M, Shah N, Folk JC. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digital Medicine. 2018;1(1):39.
15. 15. Zargoush M, Ghazalbash S, Hosseini MM, Alemi F, Perri D. Machine learning driven diabetes care using predictive-prescriptive analytics for personalized medication prescription. Scientific Reports. 2025;15(1):26811.
16. 16. de Margerie-Mellon C, Chassagnon G. Artificial intelligence: A critical review of applications for lung nodule and lung cancer. Diagn Interv Imaging. 2023;104(1):11-17.
17. 17. Georgakopoulou VE, Spandidos DA, Corlateanu A. Diagnostic tools in respiratory medicine (Review). Biomed Rep. 2025;23(1):112.
18. 18. Karthika M, Sreedharan JK, Shevade M, Mathew CS, Ray S. Artificial intelligence in respiratory care. Front Digit Health. 2024;6.
19. 19. Repici A, Badalamenti M, Maselli R, et al. Efficacy of Real-Time Computer-Aided Detection of Colorectal Neoplasia in a Randomized Trial. Gastroenterology. 2020;159(2):512-520.e7.
20. 20. Vagliano I, Chesnaye NC, Leopold JH, Jager KJ, Abu-Hanna A, Schut MC. Machine learning models for predicting acute kidney injury: a systematic review and critical appraisal. Clin Kidney J. 2022;15(12):2266-2280.
21. 21. Fleuren LM, Klausch TLT, Zwager CL, et al. Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy. Intensive Care Med. 2020;46(3):383-400.
22. 22. Mascagni P, Alapatt D, Lapergola A, et al. Early-stage clinical evaluation of real-time artificial intelligence assistance for laparoscopic cholecystectomy. Br J Surg. 2024;111(1).
23. 23. Lång K, Josefsson V, Larsson AM, et al. Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening ac…. Lancet Oncol. 2023;24(8):936-944.
24. 24. Moor M, Huang Q, Wu S, et al. Med-Flamingo: a Multimodal Medical Few-shot Learner. Proc Mach Learn Res. PMLR. 2023; 225:353-367. Accessed May 13, 2026. https://proceedings.mlr.press/v225/moor23a.html
25. 25. Zhang K, Zhou R, Adhikarla E, et al. A generalist vision–language foundation model for diverse biomedical tasks. Nat Med. 2024;30(11):3129-3141.
26. 26. 10 Promising AI Applications in Health Care. Accessed May 3, 2026. https://hbr.org/2018/05/10-promising-ai-applications-in-health-care
27. 27. Qiu J, Lam K, Li G, et al. LLM-based agentic systems in medicine and healthcare. Nature Machine Intelligence. 2024;6(12):1418-1420.
28. 28. Artificial Intelligence (AI)-Driven Virtual Nursing Assistants Market Report 2025. Accessed May 3, 2026.
29. 29. Drazen JM, Kohane IS, Leong TY, et al. Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine. N Engl J Med. 2023;388(13):1233-1239.