aceArtificial 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 identi…
AI-assisted orthodontic systems and remote monitoring platforms reduced in-person appointments while maintaining clinical standards and improving patient compliance in included studies.
Most studies of AI-enabled orthodontic care were conducted in urban or institutional environments, leaving a significant gap in real-world longitudinal data for rural or low-resource settings where access remains a major challenge.
Evidence base is concentrated in urban or institutional environments with limited longitudinal real-world validation in rural or low-resource settings.
Evidence
- Peer-reviewedInternational Journal of Dentistry2026-09-12
How should this claim be treated?
Truvace Impact Record TRV-2026-1078, v1: “Artificial Intelligence-Enabled Orthodontic Care for Remote and Underserved Populations: A Scoping Review of Access, Technology, and Public Health Integration.” Truvace, 2026-09-14. /record/TRV-2026-1078 (accessed at citation time). sha256 1950b6f802747bb1…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-1078 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.