Large AI Models in Dental Healthcare: From General-Purpose Systems to Domain-Specific Foundation Models
Quick Take
Large AI models in dental healthcare, including DentVFM and OralGPT, show promise in multimodal tasks but face challenges like data asymmetry and hallucination. Effective systems integrate general-purpose and domain-specific models within structured pipelines to enhance performance and address clinical evaluation gaps.
Key Points
- 97 studies from 2020-2026 were analyzed to assess AI models in dentistry.
- Language-generative models excel at text tasks but struggle with image diagnostics.
- Dental-specific models like DentVFM outperform others in complex multimodal tasks.
- Integrated pipelines combining models yield better results than single-model approaches.
- Key barriers include hallucination, limited datasets, and lack of evaluation benchmarks.
Article Content
From source RSS / original summaryarXiv:2606. 02914v1 Announce Type: new 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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