Sağlık Hizmetlerinde Kullanılan Yapay Zekâ Modelleri
Özet
İnsanlığın varoluşundan bu yana gelişen teknoloji, günümüzde akıl almaz bir hızla gelişmektedir. Özellikle sağlık alanındaki dijital ve teknolojik dönüşüm yalnızca bilişim uzmanlarının ilgi alanıyla sınırlı kalmamıştır. Sağlık çalışanlarının multidisipliner birlikteliği ile sağlık teknolojileriyle desteklenen yapay zekâ uygulamaları sağlık hizmetleri üzerinde büyük bir etki ve dönüşüm oluşturmaya başlamıştır. Sağlık sektörünün doğası gereği sağlık hizmetleri; biyolojik, psikolojik, sosyal, ekonomik ve idari pek çok boyutuyla oldukça karmaşık bir alan hâline gelmiştir. Aynı zamanda tedavi hizmetleri devasa ve girift veri yığınlarının hızlı ama olabildiğince hatasız analiz edilmesini gerektirir. Yapay zekâ (YZ), sağlık sektöründe karar destek mekanizmalarını yeniden tanımlayarak hasta sonuçlarını iyileştiren ve sağlık hizmeti sunum süreçlerini optimize eden bir paradigma değişimini temsil eder hale geldi. Özellikle makine öğrenimi (ML), derin öğrenme (DL), doğal dil işleme (NLP) ve yapay sinir ağları (ANN) gibi alt bileşenlerden güç alan yapay zekâ modelleri; klinik karar destek sistemlerinden tıbbi görüntü analizine, hastalık tahminlerinden kişiselleştirilmiş tedaviye kadar geniş bir uygulama alanı sunmaya başlamıştır. İnsan zihniyle çok uzun sürelerde çözülemeyecek işlemler, yapay zekâ algoritmaları ve yüksek kapasiteli işlemci gücüyle çok kısa sürelerde çözülebilir hâle gelmiştir. Google DeepMind'in geliştirdiği AlphaFold gibi yapay zekâlar, protein katlanmalarını hesaplayarak yüzlerce yıl sürecek hesaplamaları dakikalara indirmeyi başarmıştır. Akıl almaz bir hızla gelişen sağlık teknolojilerinin; tıp doktorlarının klinik deneyimleri, sağlık teknolojileri mühendislerinin sistem tasarımları ve yapay zekâ (YZ) prompt mühendislerinin modelleri profesyonelce kullanımı sayesinde birbirine entegre olduğu çok disiplinli bir yaklaşımı ortaya koymaktadır. Bu bölümde, sağlık hizmetlerinde kullanılan yapay zekâ modelleri; teknik temelleri, klinik uygulama örnekleri ve gelecek perspektifleri ışığında analiz edilmeye çalışılacaktır. Örnek olarak; CNN (Convolutional Neural Networks) temelli görüntü tanıma sistemleri, LSTM (Long Short-Term Memory) ile hasta verisi üzerinden zaman serisi tahminleri, XGBoost modelleriyle bireysel risk skorlama algoritmaları ve Transformer tabanlı dil modelleriyle dijital sağlık asistanları ele alınacaktır.
Since the dawn of humanity, technology has evolved, and today it is advancing at an astonishing pace. Particularly in the field of healthcare, the digital and technological transformation has expanded beyond the domain of information technology specialists. With the multidisciplinary collaboration of healthcare professionals, artificial intelligence (AI) applications supported by health technologies have begun to create a significant impact and transformation in healthcare services.
By its very nature, the healthcare sector has become an extremely complex field, encompassing biological, psychological, social, economic, and administrative dimensions. Moreover, treatment services require the rapid yet highly accurate analysis of massive and intricate datasets. Artificial intelligence (AI) now represents a paradigm shift in the healthcare industry by redefining decision support mechanisms, improving patient outcomes, and optimizing healthcare delivery processes.
AI models, particularly those powered by subfields such as machine learning (ML), deep learning (DL), natural language processing (NLP), and artificial neural networks (ANN), have started to offer a wide range of applications from clinical decision support systems to medical image analysis, disease prediction, and personalized treatment. Tasks that would take the human mind an extraordinary amount of time can now be solved in mere moments using AI algorithms and high-capacity processing power. For example, AI models like AlphaFold, developed by Google DeepMind, have succeeded in reducing protein-folding computations that would otherwise take centuries into a matter of minutes.
The rapidly evolving healthcare technologies showcase a multidisciplinary approach in which the clinical experience of physicians, system designs by healthcare technology engineers, and the professional use of AI prompt engineering are seamlessly integrated. In this chapter, AI models used in healthcare services will be analyzed in terms of their technical foundations, clinical application examples, and future perspectives. Examples to be discussed include image recognition systems based on Convolutional Neural Networks (CNNs), time series forecasting of patient data using Long Short-Term Memory (LSTM) networks, individual risk scoring algorithms with XGBoost models, and digital health assistants built on Transformer-based language models.
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