Artificial Intelligence and Dentistry: A Clinical Review and a Proposed CHAIR Framework

Abstract

Objective: Artificial intelligence (AI) is entering dental practice through imaging software, screening initiatives and patient-facing chatbots, yet clinicians have limited concise guidance on how to evaluate these tools. This review summarises where AI now meets dentistry and offers a practical method for appraising a tool before chairside use. 

Materials and Methods: We conducted a narrative review of peer-reviewed studies, regulatory texts and reporting guidelines, favouring work that was validated beyond its own development sample, deployed in real settings or explicit about bias and governance. The evidence is organised along the clinical pathway and paired with a proposed five-point checklist (the CHAIR framework) and a worked example. 

Results: The strongest evidence lies in radiographic image analysis. Convolutional neural networks detect caries on bitewings, measure periodontal and periapical bone loss and segment teeth and bone on cone-beam computed tomography at a level comparable with experienced clinicians. Photograph-based models screen for oral cancer and its precursor lesions, while other systems assist cephalometric analysis, extraction planning and implant identification. Language models are starting to help with documentation and patient communication. Accuracy is highest on tightly defined tasks and usually declines when the scanner, patient group or disease distribution differs from the system's training data; reliable testing in new clinical settings remains uncommon. 

Conclusion: AI is most useful when framed as augmented intelligence: a support for the dentist's judgement, not a substitute. Safe adoption depends on appraising each tool against the local setting, monitoring for bias and putting clear governance in place.

Keywords

• Artificial intelligence
• Clinical decision support systems
• Dentistry
• Risk management
• Workflow integration

Yapay Zekâ ve Diş Hekimliği: Bir Klinik Derleme ve Çerçeve Önerisi (CHAIR)

Abstract

Amaç: Yapay zekâ (YZ), görüntüleme yazılımları, tarama girişimleri ve hastaya yönelik sohbet robotları aracılığıyla diş hekimliği pratiğine girmektedir. Diğer yandan, klinisyenlerin bu araçları nasıl değerlendireceğine ilişkin kısa ve uygulanabilir rehberlik hâlen sınırlıdır. Bu derleme, YZ’nin diş hekimliğiyle güncel kesişim alanlarını özetlemekte ve bir aracın klinik kullanım öncesinde değerlendirilmesi için pratik bir yöntem sunmaktadır. 

Gereç ve Yöntemler: Hakemli çalışmalar, düzenleyici metinler ve raporlama kılavuzları temel alınarak anlatısal bir derleme yapılmıştır. Kendi geliştirme veri kümesi dışında doğrulanmış, prospektif olarak değerlendirilmiş, gerçek klinik ortamlarda uygulanmış ya da yanlılık ve yönetişim konularını açıkça ele almış çalışmalara öncelik verilmiştir. Kanıtlar klinik bakım akışı boyunca düzenlenmiş; önerilen beş maddelik bir kontrol listesi (CHAIR çerçevesi) ve uygulamalı bir örnekle birlikte sunulmuştur. 

Bulgular: En güçlü kanıtlar radyografik görüntü analizinde yoğunlaşmaktadır. Evrişimli sinir ağları, bite-wing radyografilerinde çürük lezyonlarını saptamakta, periodontal ve periapikal kemik kaybını ölçmekte ve konik ışınlı bilgisayarlı tomografide diş ve kemik dokularının segmentasyonunu deneyimli hekimlerle karşılaştırılabilir doğrulukta gerçekleştirmektedir. Fotoğraf tabanlı modeller ağız kanseri ve öncül lezyonların taranmasına katkı sağlarken, diğer sistemler sefalometrik analiz, çekim planlaması ve implant tanımlamayı desteklemektedir. Dil modelleri klinik dokümantasyon ve hasta iletişiminde yardımcı olmaya başlamıştır. Doğruluğun dar tanımlı görevlerde en yüksek olduğu; tarayıcı, hasta grubu veya hastalık dağılımı sistemin eğitim verilerinden uzaklaştığında genellikle düştüğü anlatılmaktadır. Yeni bir klinik ortamda güvenilir doğrulama uygulamaları ise hâlen nadirdir. 

Sonuç: YZ en yararlı biçimde, diş hekiminin kararını destekleyen ancak onun yerini almayan artırılmış zekâ olarak değerlendirilmelidir. Güvenli kullanım, her aracın yerel koşullara göre değerlendirilmesine, yanlılık açısından izlenmesine ve açık yönetişim mekanizmalarının kurulmasına bağlıdır.

Keywords

• Diş hekimliği
• İş akışı entegrasyonu
• Risk yönetimi
• Sağlık teknolojilerinin benimsenmesi
• Yapay zekâ

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