Artificial Intelligence-Enabled Orthodontic Care for Remote and Underserved Populations: A Scoping Review of Access, Technology, and Public Health Integration

Artificial Intelligence-Enabled Orthodontic Care for Remote and Underserved Populations: A Scoping Review of Access, Technology, and Public Health Integration. BACKGROUND: Unequal access to orthodontic care remains a major public health challenge, particularly in rural and underserved regions, recent advancements in artificial intelligence (AI) and digital orthodontics offer potential to bridge these gaps through remote diagnosis and monitoring. AIM: This scoping review aims to systematically map and categorize how AI applications in orthodontic diagnosis, treatment planning, and remote monitoring enhance access to care for underserved and remote populations while identifying specific access-related outcomes, research gaps, and policy implications for public health integration. METHODS: This scoping review followed the Arksey and O'Malley framework and was reported in accordance with the preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews (PRISMA-ScR) guidelines. A comprehensive search was conducted across PubMed, Scopus, Embase, and Web of Science, supplemented with relevant gray literature. Studies published between January 2000 and September 2025 that discussed the use of AI in orthodontic diagnosis, treatment planning, appliance design, or teledentistry-based service delivery were included. Two independent reviewers performed screening and data extraction using Rayyan, and results were synthesized descriptively. RESULTS: A total of 23 studies met the inclusion criteria. The evidence indicated that AI-assisted orthodontic systems can enhance diagnostic precision and reduce clinical workload. Remote monitoring platforms were shown to reduce in-person appointments while maintaining clinical standards and improving patient compliance. However, majority of these studies were conducted in urban or institutional environments, highlighting a significant gap in real-world longitudinal data for rural or low-resource settings. C…