Yapay Zekâ Temel İlkeleri ve Diş Hekimliğinde Kullanım Alanları

Öz

Özet Amaç: Bu derlemenin amacı, yapay zekânın temel kavramsal çerçevesini, makine öğrenmesi ve derin öğrenme yaklaşımları temelinde ele almak ve bu teknolojilerin diş hekimliğindeki güncel kullanım alanlarını literatür doğrultusunda değerlendirmektir. Bu kapsamda yapay zekâ uygulamalarının tanı, tedavi planlaması, görüntü analizi, prognoz tahmini, klinik karar destek sistemleri ve hasta yönetimi süreçlerindeki potansiyel katkılarının ortaya konulması hedeflenmiştir. Tartışma: Güncel literatür, yapay zekâ uygulamalarının diş hekimliğinde tanı, görüntü analizi, tedavi planlaması ve klinik karar destek süreçlerinde giderek daha önemli bir yer edindiğini göstermektedir. Makine öğrenmesi algoritmaları, yapılandırılmış klinik verilerden anlamlı örüntüler elde ederek hastalık risklerinin belirlenmesi, prognoz değerlendirmesi ve tedavi sonuçlarının öngörülmesine katkı sağlamaktadır. Derin öğrenme yaklaşımları ise özellikle dental radyografiler, CBCT verileri ve ağız içi görüntüler üzerinden anatomik yapıların segmentasyonu ve patolojik bulguların tespitinde yüksek potansiyel sunmaktadır. Bununla birlikte, bu sistemlerin klinik uygulamalara güvenilir biçimde aktarılabilmesi için geniş ve standartlaştırılmış veri setlerine, dış doğrulama çalışmalarına ve etik ilkelere uygun algoritmalara ihtiyaç duyulmaktadır. Bu nedenle yapay zekâ, klinik kararın yerine geçen bağımsız bir sistemden ziyade, diş hekimliğinde tanısal doğruluğu ve tedavi öngörülebilirliğini destekleyen yardımcı bir araç olarak değerlendirilmelidir. Sonuç: Yapay zekâ, makine öğrenmesi ve derin öğrenme temelli yaklaşımlar aracılığıyla diş hekimliğinde tanı, tedavi planlaması, görüntü analizi ve klinik karar destek süreçlerine önemli katkılar sunma potansiyeline sahiptir. Bununla birlikte, bu sistemlerin klinik uygulamalarda güvenilir biçimde kullanılabilmesi için standartlaştırılmış veri setleri, dış doğrulama çalışmaları ve etik ilkelere uygun algoritmalara gereksinim bulunmaktadır. Sonuç olarak yapay zekâ, klinisyenin kararını destekleyen yardımcı bir teknolojik araç olarak değerlendirilmelidir.

Anahtar Kelimeler

• Yapay Zekâ
• Makine Öğrenimi
• Derin Öğrenme
• Diş Hekimliği

Basic Principles of Artificial Intelligence and Its Applications in Dentistry

Öz

Abstract Objective: The aim of this review is to evaluate the fundamental conceptual framework of artificial intelligence on the basis of machine learning and deep learning approaches, and to assess the current applications of these technologies in dentistry in line with the literature. In this context, this review aims to highlight the potential contributions of artificial intelligence applications to diagnosis, treatment planning, image analysis, prognosis prediction, clinical decision support systems, and patient management processes. Discussion: Current literature indicates that artificial intelligence applications are gaining increasing importance in dentistry, particularly in diagnosis, image analysis, treatment planning, and clinical decision support processes. Machine learning algorithms contribute to the identification of disease risks, prognosis assessment, and prediction of treatment outcomes by extracting meaningful patterns from structured clinical data. Deep learning approaches, on the other hand, show considerable potential in the segmentation of anatomical structures and the detection of pathological findings, especially through dental radiographs, CBCT data, and intraoral images. However, for these systems to be reliably integrated into clinical practice, large and standardized datasets, external validation studies, and algorithms developed in accordance with ethical principles are required. Therefore, artificial intelligence should be considered not as an independent system replacing clinical decision-making, but as an auxiliary tool that supports diagnostic accuracy and treatment predictability in dentistry. Conclusion: Artificial intelligence has the potential to make significant contributions to diagnosis, treatment planning, image analysis, and clinical decision support processes in dentistry through machine learning- and deep learning-based approaches. However, standardized datasets, external validation studies, and algorithms developed in accordance with ethical principles are required for the reliable use of these systems in clinical practice. In conclusion, artificial intelligence should be regarded as an auxiliary technological tool that supports the clinician’s decision-making process.

Anahtar Kelimeler

• Artificial Intelligence
• Machine Learning
• Deep Learning
• Dentistry

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