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

2026-06-01 · Source: Artificial Intelligence · Field: Health & Wellbeing — Artificial Intelligence & Machine Learning, Clinical Care & Medical Practice, Health & Medical Research · Depth: Advanced, quick

Summary

A systematic review of 97 studies published between 2020 and 2026 investigates the clinical potential of large-scale AI models in dentistry, addressing oral diseases affecting nearly 3.5 billion people worldwide. The review categorizes models into language-generative, discriminative vision foundation models, and dental-specific foundation models, proposing a two-dimensional classification framework. Findings indicate language-generative models excel in text-based tasks like clinical reasoning and patient communication but struggle with image-dependent diagnostics. Adapted SAM and CLIP variants show strong performance in tooth segmentation and lesion detection. Dental-specific models such as DentVFM, DentVLM, and OralGPT achieve the strongest results on complex multimodal tasks, with integrated pipelines consistently outperforming single-model approaches. A notable data asymmetry exists, with dental-specific pretraining predominantly in the vision domain due to scarce large-scale dental text corpora.

Key takeaway

For AI Scientists developing dental healthcare solutions, you should prioritize integrated pipelines that combine general-purpose and dental-specific models to achieve optimal performance on complex multimodal tasks. Be aware that current dental-specific pretraining heavily favors vision data, indicating a need for more large-scale dental text corpora. Address persistent barriers like hallucination, limited annotated datasets, and absent standardized clinical evaluation benchmarks before considering autonomous deployment.

Key insights

Effective dental AI systems combine general-purpose and dental-specific models within structured pipelines.

Principles

• Integrated AI pipelines outperform single models.
• Dental-specific models excel in multimodal tasks.
• Vision data dominates dental AI pretraining.

Method

A systematic review followed PRISMA-ScR guidelines, searching PubMed, Google Scholar, Scopus, and arXiv, then screening 97 studies (2020-2026) to classify models by architecture and specialization.

In practice

• Use language models for text-based dental tasks.
• Apply SAM/CLIP for tooth segmentation.
• Consider DentVFM/DentVLM for multimodal tasks.

Topics

• Dental AI
• Foundation Models
• Multimodal AI
• Clinical Evaluation
• Oral Healthcare
• Medical Imaging AI

Best for: Computer Vision Engineer, AI Scientist, Research Scientist, Domain Expert

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.