Large AI Models in Dental Healthcare: From General-Purpose Systems to Domain-Specific Foundation Models

• URL: http://arxiv.org/abs/2606.02914v2
• Date: Wed, 03 Jun 2026 03:01:03 GMT
• Title: Large AI Models in Dental Healthcare: From General-Purpose Systems to Domain-Specific Foundation Models
• Abstract summary: Oral diseases affect nearly 3.5 billion people worldwide, yet the comparative clinical potential of large-scale AI models in dentistry remains poorly understood.<n>Three distinct model categories have emerged: language-generative models, discriminative vision foundation models, and dental-specific models.<n>General-purpose and dental-specific models play complementary roles; the most effective systems combine both within structured pipelines.
• Score: 0.30650083373343306
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Background: Oral diseases affect nearly 3.5 billion people worldwide, yet the comparative clinical potential of large-scale AI models in dentistry remains poorly understood. Three distinct model categories have emerged: language-generative models, discriminative vision foundation models, and dental-specific foundation models, with no unified review examining their relationships and collective limitations. Methods: Following PRISMA-ScR guidelines, we systematically searched four databases (PubMed, Google Scholar, Scopus, arXiv), screened independently by two reviewers. After applying inclusion/exclusion criteria, 97 studies (2020-2026) were included. We propose a two-dimensional classification framework organizing models by architectural paradigm and dental specialization degree. Results: Language-generative models excel at text-based tasks (clinical reasoning, licensing exams, patient communication) but show inconsistent performance on image-dependent diagnostics. Adapted SAM and CLIP variants achieve strong tooth segmentation and lesion detection results. Dental-specific models (DentVFM, DentVLM, OralGPT) demonstrate strongest performance on complex multimodal tasks. Integrated pipelines consistently outperform single-model approaches. A data asymmetry is observed: dental-specific pretraining concentrates almost entirely in the vision domain, reflecting scarce large-scale dental text corpora. Conclusions: General-purpose and dental-specific models play complementary roles; the most effective systems combine both within structured pipelines. Safe autonomous deployment requires resolving three persistent barriers: hallucination in generative models, limited annotated dental datasets, and absent standardized clinical evaluation benchmarks.

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