Yapay Zekâ ve Makine Öğrenmesinin Sağlık Alanında Etkileri
Özet
Yapay zekâ (YZ) ve makine öğrenmesi, sağlık alanında hızla gelişen ve klinik süreçleri kökten dönüştüren yenilikçi teknolojiler arasında yer almaktadır. Alan Turing’in “Computing Machinery and Intelligence” çalışması ve 1956 Dartmouth Konferansı ile temelleri atılan YZ; günümüzde makine öğrenmesi, derin öğrenme ve yapay sinir ağları aracılığıyla sağlık hizmetlerinin birçok alanında uygulanmaktadır. Özellikle radyoloji, patoloji, kardiyoloji ve diş hekimliği gibi disiplinlerde tanısal doğruluğun artırılması, tedavi planlamasının kişiselleştirilmesi ve klinik karar destek sistemlerinin güçlendirilmesi açısından önemli katkılar sağlamaktadır. Derin öğrenme tabanlı modeller, büyük veri setlerini analiz ederek erken teşhis, hastalık sınıflandırması ve risk değerlendirmelerinde yüksek doğruluk sunmaktadır. Ayrıca ilaç geliştirme, genomik analizler, elektronik sağlık kayıtlarının yönetimi ve hasta güvenliğinin artırılmasında da etkin bir rol üstlenmektedir. Ancak YZ’nin sağlık alanındaki entegrasyonu yalnızca teknik yeniliklerle sınırlı değildir; hasta mahremiyetinin korunması, algoritmik önyargıların önlenmesi, şeffaflık ve hesap verilebilirlik gibi etik ve hukuki boyutlar da kritik öneme sahiptir. Avrupa Birliği Yapay Zekâ Yasası (2024) ve ABD’deki düzenlemeler, bu teknolojilerin güvenli ve sorumlu kullanımını sağlamak için önemli çerçeveler sunmaktadır. Sonuç olarak, yapay zekâ ve makine öğrenmesi tabanlı yaklaşımlar; sağlık hizmetlerinde daha hızlı, güvenilir, maliyet etkin ve kişiselleştirilmiş çözümler sunarak geleceğin klinik uygulamalarına yön verecek stratejik bir potansiyele sahiptir.
Referanslar
Ahmed, N., Abbasi, M. S., Zuberi, F., Qamar, W., Halim, M. S. B., Maqsood, A., & Alam, M. K. (2021). Artificial intelligence techniques: analysis, application, and outcome in dentistry—a systematic review. BioMed Research International, 2021(1), 9751564.
Alukić, E., Homar, K., Pavić, M., Žibert, J., & Mekiš, N. (2023). The impact of subjective image quality evaluation in mammography. Radiography, 29(3), 526-532.
Aminoshariae, A., Kulild, J., & Nagendrababu, V. (2021). Artificial intelligence in endodontics: current applications and future directions. Journal of endodontics, 47(9), 1352-1357.
Arulkumaran, K., Deisenroth, M. P., Brundage, M., & Bharath, A. A. (2017). Deep reinforcement learning: A brief survey. IEEE signal processing magazine, 34(6), 26-38.
[Record #633 is using a reference type undefined in this output style.]
Benko, A., & Lányi, C. S. (2009). History of artificial intelligence. In Encyclopedia of Information Science and Technology, Second Edition (pp. 1759-1762). IGI global.
Burns, M. (2024). Challenges and successes in implementing an integrated electronic patient record (HIVE) at the Manchester University National Health Service Foundation Trust, England: 1000+ legacy systems, 10 hospitals, one electronic patient record. Health Information Management Journal, 53(1), 20-28.
Buşoniu, L., Babuška, R., & De Schutter, B. (2010). Multi-agent reinforcement learning: An overview. Innovations in multi-agent systems and applications-1, 183-221.
Campbell, M., Hoane Jr, A. J., & Hsu, F.-h. (2002). Deep blue. Artificial intelligence, 134(1-2), 57-83.
Cord, M., & Cunningham, P. (2008). Machine learning techniques for multimedia: case studies on organization and retrieval. Springer Science & Business Media.
Crevier, D. (1993). AI: the tumultuous history of the search for artificial intelligence. Basic Books, Inc.
Dara, S., Dhamercherla, S., Jadav, S. S., Babu, C. M., & Ahsan, M. J. (2022). Machine learning in drug discovery: a review. Artificial intelligence review, 55(3), 1947-1999.
Deng, L., & Yu, D. (2014). Deep learning: methods and applications. Foundations and trends® in signal processing, 7(3–4), 197-387.
Dike, H. U., Zhou, Y., Deveerasetty, K. K., & Wu, Q. (2018). Unsupervised learning based on artificial neural network: A review. 2018 IEEE International Conference on Cyborg and Bionic Systems (CBS),
Dimiduk, D. M., Holm, E. A., & Niezgoda, S. R. (2018). Perspectives on the impact of machine learning, deep learning, and artificial intelligence on materials, processes, and structures engineering. Integrating Materials and Manufacturing Innovation, 7(3), 157-172.
Goldman, L., Cook, E. F., Johnson, P. A., Brand, D. A., Rouan, G. W., & Lee, T. H. (1996). Prediction of the need for intensive care in patients who come to emergency departments with acute chest pain. New England Journal of Medicine, 334(23), 1498-1504.
Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning (Vol. 1). MIT press Cambridge.
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., & Cuadros, J. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. jama, 316(22), 2402-2410.
Gündüz, G., & Cedimoğlu, İ. H. (2019). Derin öğrenme algoritmalarını kullanarak görüntüden cinsiyet tahmini. Sakarya University Journal of Computer and Information Sciences, 2(1), 9-17.
Hardy, M., & Harvey, H. (2020). Artificial intelligence in diagnostic imaging: impact on the radiography profession. The British journal of radiology, 93(1108), 20190840.
Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. (2018). Artificial intelligence in radiology. Nature Reviews Cancer, 18(8), 500-510.
İnik, Ö., & Ülker, E. (2017). Derin öğrenme ve görüntü analizinde kullanılan derin öğrenme modelleri. Gaziosmanpaşa Bilimsel Araştırma Dergisi, 6(3), 85-104.
Kaur, N., Jacob, G., Singh, A., Khan, S., Dhir, P., & Kakarla, G. (2025). Artificial Intelligence in dentistry: Balancing innovation with ethical responsibility. Bioinformation, 21(3), 489.
Kelly, B. S., Judge, C., Bollard, S. M., Clifford, S. M., Healy, G. M., Aziz, A., Mathur, P., Islam, S., Yeom, K. W., & Lawlor, A. (2022). Radiology artificial intelligence: a systematic review and evaluation of methods (RAISE). European radiology, 32(11), 7998-8007.
Khanna, S. S., & Dhaimade, P. A. (2017). Artificial intelligence: transforming dentistry today. Indian J Basic Appl Med Res, 6(3), 161-167.
Kim, C. S., Samaniego, C. S., Sousa Melo, S. L., Brachvogel, W. A., Baskaran, K., & Rulli, D. (2023). Artificial intelligence (AI) in dental curricula: Ethics and responsible integration. Journal of dental education, 87(11), 1570-1573.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25.
Lee, J.-G., Jun, S., Cho, Y.-W., Lee, H., Kim, G. B., Seo, J. B., & Kim, N. (2017). Deep learning in medical imaging: general overview. Korean journal of radiology, 18(4), 570-584.
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., Van Der Laak, J. A., Van Ginneken, B., & Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical image analysis, 42, 60-88.
McCarthy, J. (1960). Recursive functions of symbolic expressions and their computation by machine, part I. Communications of the ACM, 3(4), 184-195.
McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955. AI magazine, 27(4), 12-12.
Minaee, S., Boykov, Y., Porikli, F., Plaza, A., Kehtarnavaz, N., & Terzopoulos, D. (2021). Image segmentation using deep learning: A survey. IEEE transactions on pattern analysis and machine intelligence, 44(7), 3523-3542.
Mintz, Y., & Brodie, R. (2019). Introduction to artificial intelligence in medicine. Minimally Invasive Therapy & Allied Technologies, 28(2), 73-81.
Naeem, M. M., Sarwar, H., Hassan, M. T., Balouch, N. M., Singh, S. P., Essrani, P. D., & Rajper, P. (2023). Exploring the ethical and privacy implications of artificial intelligence in dentistry. International Journal of Health Sciences, 7(S1), 904-915.
Nasteski, V. (2017). An overview of the supervised machine learning methods. Horizons. b, 4(51-62), 56.
Nichols, J. A., Herbert Chan, H. W., & Baker, M. A. (2019). Machine learning: applications of artificial intelligence to imaging and diagnosis. Biophysical reviews, 11(1), 111-118.
Olsen, T. G., Jackson, B. H., Feeser, T. A., Kent, M. N., Moad, J. C., Krishnamurthy, S., Lunsford, D. D., & Soans, R. E. (2018). Diagnostic performance of deep learning algorithms applied to three common diagnoses in dermatopathology. Journal of pathology informatics, 9(1), 32.
Ongsulee, P. (2017). Artificial intelligence, machine learning and deep learning. 2017 15th international conference on ICT and knowledge engineering (ICT&KE),
Özsezen, A. (2021). Yapay Zeka ve Derin Öğrenme Teknolojileri ile Kalça Eklemi Radyografilerinde Femoral Komponentin Tanınması. Sağlık Bilimleri Üniversitesi, Gülhane Tıp Fakültesi, Tıpta Uzmanlık Tezi.
Öztemel, E. (2012). Yapay sinir ağları. Papatya.
Öztürk, K., & Şahin, M. E. (2018). Yapay sinir ağları ve yapay zekâ’ya genel bir bakış. Takvim-i Vekayi, 6(2), 25-36.
Park, S. H., Do, K.-H., Kim, S., Park, J. H., & Lim, Y.-S. (2019). What should medical students know about artificial intelligence in medicine? Journal of educational evaluation for health professions, 16.
Rajpurkar, P., Irvin, J., Ball, R. L., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., & Langlotz, C. P. (2018). Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS medicine, 15(11), e1002686.
Schwalbe, N., & Wahl, B. (2020). Artificial intelligence and the future of global health. The Lancet, 395(10236), 1579-1586.
Schwendicke, F. a., Samek, W., & Krois, J. (2020). Artificial intelligence in dentistry: chances and challenges. Journal of Dental Research, 99(7), 769-774.
Sejnowski, T. J. (2020). The unreasonable effectiveness of deep learning in artificial intelligence. Proceedings of the National Academy of Sciences, 117(48), 30033-30038.
Serrano, D. R., Luciano, F. C., Anaya, B. J., Ongoren, B., Kara, A., Molina, G., Ramirez, B. I., Sánchez-Guirales, S. A., Simon, J. A., & Tomietto, G. (2024). Artificial intelligence (AI) applications in drug discovery and drug delivery: Revolutionizing personalized medicine. Pharmaceutics, 16(10), 1328.
Shah, R. F., Martinez, A. M., Pedoia, V., Majumdar, S., Vail, T. P., & Bini, S. A. (2019). Variation in the thickness of knee cartilage. The use of a novel machine learning algorithm for cartilage segmentation of magnetic resonance images. The Journal of arthroplasty, 34(10), 2210-2215.
Shortliffe, E. (2012). Computer-based medical consultations: MYCIN (Vol. 2). Elsevier.
Skansi, S. (2018). Introduction to Deep Learning: from logical calculus to artificial intelligence. Springer.
Someeh, N., Jafarabadi, M. A., Shamshirgaran, S. M., & Farzipoor, F. (2020). The outcome in patients with brain stroke: A deep learning neural network modeling. Journal of Research in Medical Sciences, 25(1), 78.
Suzuki, K. (2017). Overview of deep learning in medical imaging. Radiological physics and technology, 10(3), 257-273.
Thong, W., Parent, S., Wu, J., Aubin, C.-E., Labelle, H., & Kadoury, S. (2016). Three-dimensional morphology study of surgical adolescent idiopathic scoliosis patient from encoded geometric models. European spine journal, 25(10), 3104-3113.
Turing, A. M. (2007). Computing machinery and intelligence. In Parsing the Turing test: Philosophical and methodological issues in the quest for the thinking computer (pp. 23-65). Springer.
Wang, Z., Keane, P. A., Chiang, M., Cheung, C. Y., Wong, T. Y., & Ting, D. S. W. (2022). Artificial intelligence and deep learning in ophthalmology. In Artificial intelligence in medicine (pp. 1519-1552). Springer.
Weiner, E. B., Dankwa-Mullan, I., Nelson, W. A., & Hassanpour, S. (2025). Ethical challenges and evolving strategies in the integration of artificial intelligence into clinical practice. PLOS digital health, 4(4), e0000810.
Zhou, W., Zhang, X., Ding, J., Deng, L., Cheng, G., & Wang, X. (2023). Improved breast lesion detection in mammogram images using a deep neural network. Diagnostic and Interventional Radiology, 29(4), 588.
