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

A systematic review of 97 studies identifies three categories of AI models in dentistry—language-generative, vision foundation, and dental-specific models—finding that integrated pipelines combining general-purpose and specialized systems deliver optimal performance. The research reveals critical deployment barriers including model hallucination, scarce annotated dental datasets, and absent clinical evaluation standards.

This systematic review addresses a significant gap in understanding how large AI models can be applied to dental healthcare, where oral diseases affect 3.5 billion people globally. The research categorizes three distinct AI model types and evaluates their clinical potential through analysis of 97 peer-reviewed studies from 2020-2026, providing rare comparative insights into their relative strengths and weaknesses.

The findings reveal a clear performance hierarchy: general-purpose language models handle text-based clinical reasoning and patient communication well but struggle with image-dependent diagnostics. Vision models adapted from broader AI systems—particularly SAM and CLIP variants—show strong results in tooth segmentation and lesion detection. Dental-specific foundation models (DentVFM, DentVLM, OralGPT) demonstrate superior performance on complex multimodal tasks that combine vision and language understanding. Critically, integrated pipelines combining multiple model types consistently outperform single-model approaches.

The research identifies a structural asymmetry in dental AI development: pretraining data concentrates almost entirely in the vision domain due to scarcity of large-scale dental text corpora. This imbalance constrains model development and limits the potential of multimodal approaches. The study establishes three concrete barriers to safe clinical deployment: persistent hallucination in generative models, insufficient annotated dental datasets for training, and absence of standardized benchmarks for clinical evaluation.

These findings suggest the dental AI market requires ecosystem development beyond model architecture. Organizations investing in dental-specific foundation models must simultaneously address data infrastructure, standardized evaluation frameworks, and clinical validation protocols to enable real-world deployment.

• →Integrated pipelines combining general-purpose and dental-specific AI models outperform single-model systems across clinical tasks.
• →Vision-based AI models achieve strong performance in tooth segmentation and lesion detection, while language models excel at clinical reasoning.
• →Dental-specific foundation models (DentVFM, DentVLM, OralGPT) deliver superior multimodal performance but require larger annotated datasets.
• →Data asymmetry in dental AI development concentrates pretraining entirely in vision, reflecting scarcity of large-scale dental text corpora.
• →Three persistent barriers block autonomous clinical deployment: model hallucination, limited annotated datasets, and absent standardized evaluation benchmarks.