Smartface.Ai: The Concept Of An Intelligent Platform Based On Artificial Intelligence To Increase The Availability Of Dental And Maxillofacial Care In Remote Regions In Kyrgyzstan And Lmics
Artificial intelligence; dentistry; maxillofacial surgery; deep learning; diagnostic imaging; orthopantomography; telemedicine; Kyrgyz Republic; limited resources.
AuthorsAbstractLate diagnosis of oral and maxillofacial diseases remains a pressing challenge in modern healthcare, particularly in resource-limited settings. In the Kyrgyz Republic, the high prevalence of advanced-stage caries and maxillofacial pathologies is driven by a shortage of specialized personnel, limited access to expert care, and inadequate digitalization of primary healthcare. This study aimed to systematically analyze current evidence on artificial intelligence (AI) applications in diagnosing dental and maxillofacial diseases and to develop the conceptual architecture of a locally adapted intelligent platform, SmartFace.AI. We performed a systematic literature review across PubMed, Scopus, Web of Science, and Google Scholar (2019–2026). Conceptual modeling of the platform architecture leveraged deep learning methods, including convolutional neural networks and the U-Net architecture. Potential clinical efficacy was estimated by extrapolating international data to the local healthcare context. Of 42 analyzed publications, 13 high-quality studies were included in the final synthesis. Current AI algorithms demonstrate caries detection accuracies of 82–95%, retained/impacted tooth segmentation reaching an Intersection-over-Union (IoU) of 85.29%, and orthopantomogram analysis comparable to expert specialist assessment. We developed a conceptual SmartFace.AI model integrating modules for clinical image analysis, radiographic data interpretation, and telemedicine services. Implementation is projected to enhance early diagnostic accuracy by 20–35% and reduce diagnostic turnaround time by 25–40%. SmartFace.AI thus represents a promising strategy to improve the accessibility and quality of specialized dental and maxillofacial care in resource-constrained environments. These findings provide a scientific rationale for future pilot clinical trials and the practical integration of digital health technologies into routine healthcare delivery.
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