Large AI Models in Dental Healthcare: From General-Purpose Systems to Domain-Specific Foundation Models
arXiv:2606.02914v1 Announce Type: cross 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 indepen
The proliferation of various large AI models necessitates a structured comparison to understand their applicability and limitations in specialized domains like healthcare.
This research highlights the critical evolution of general-purpose AI to domain-specific foundation models, indicating a maturing of AI applications with significant implications for specialized industries.
The explicit recognition and systematic review of domain-specific AI foundation models for dentistry suggest a future where such specialized models will be critical for high-stakes applications, moving beyond general-purpose systems.
- · Dental AI startups
- · Healthcare technology providers
- · Patients receiving dental care
- · AI research institutions
- · Traditional diagnostic software providers
- · AI companies focusing solely on general models
- · Healthcare providers resistant to AI integration
Systematic reviews will accelerate the development and validation of domain-specific AI models, particularly in medical fields.
This specialization will lead to improved diagnostic accuracy and treatment planning in dentistry, potentially reducing costs and improving patient outcomes.
The success of domain-specific AI in dentistry could serve as a blueprint for other medical specialties, driving a broader shift towards specialized AI foundation models across healthcare.
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