{"slug": "large-ai-models-in-dental-healthcare-from-general-purpose-systems-to-domain", "title": "Large AI Models in Dental Healthcare: From General-Purpose Systems to Domain-Specific Foundation Models", "summary": "A systematic review of 97 studies from 2020 to 2026 found that large AI models in dentistry fall into three categories—language-generative, discriminative vision, and dental-specific foundation models—each with distinct strengths and weaknesses. Language models excel at text-based tasks like clinical reasoning but struggle with image diagnostics, while dental-specific models such as DentVFM and OralGPT outperform general-purpose systems on complex multimodal tasks. The findings highlight that safe autonomous deployment remains hindered by generative model hallucination, scarce annotated dental data, and a lack of standardized clinical benchmarks.", "body_md": "arXiv:2606.02914v1 Announce Type: new\nAbstract: 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.\nMethods: 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.\nResults: 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.\nConclusions: 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.", "url": "https://wpnews.pro/news/large-ai-models-in-dental-healthcare-from-general-purpose-systems-to-domain", "canonical_source": "https://arxiv.org/abs/2606.02914", "published_at": "2026-06-03 04:00:00+00:00", "updated_at": "2026-06-03 04:17:42.802546+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "computer-vision", "natural-language-processing"], "entities": ["DentVFM", "DentVLM", "OralGPT", "SAM", "CLIP", "PRISMA-ScR", "PubMed", "Google Scholar"], "alternates": {"html": "https://wpnews.pro/news/large-ai-models-in-dental-healthcare-from-general-purpose-systems-to-domain", "markdown": "https://wpnews.pro/news/large-ai-models-in-dental-healthcare-from-general-purpose-systems-to-domain.md", "text": "https://wpnews.pro/news/large-ai-models-in-dental-healthcare-from-general-purpose-systems-to-domain.txt", "jsonld": "https://wpnews.pro/news/large-ai-models-in-dental-healthcare-from-general-purpose-systems-to-domain.jsonld"}}