EC Dental Science

Background: Teledentistry has expanded opportunities for remote oral-health consultation, screening, monitoring, education, and referral. In parallel, artificial intelligence (AI), including machine-learning and deep-learning approaches, is increasingly being investigated for dental image analysis, disease detection, risk assessment, and clinical decision support. Combining AI with teledentistry may provide new approaches to early oral-disease screening and prioritization, particularly for populations facing geographic, socioeconomic, mobility, or workforce-related barriers to conventional dental services.

Objective: This narrative review examines current and emerging applications of AI-enabled teledentistry for early oral-disease detection and clinical triage, with particular consideration of underserved and vulnerable populations. It further discusses diagnostic limitations, implementation barriers, ethical and legal considerations, and proposes a human-in-the-loop clinical pathway for responsible integration of AI-assisted remote screening into dental care.

Methods: Relevant literature was identified through PubMed, with priority given to systematic and scoping reviews, diagnostic-accuracy studies, clinical investigations, and recent literature addressing artificial intelligence, teledentistry, oral health, remote screening, and underserved populations. The literature was assessed narratively according to clinical application, diagnostic limitations, implementation considerations, and relevance to underserved populations. As this is a narrative review, no formal systematic review protocol, meta-analysis, or quantitative pooling of results was undertaken.

Results and Discussion: Current literature demonstrates increasing investigation of AI for analysis of oral photographs, including caries, plaque and gingival conditions, while teledentistry provides mechanisms for remote acquisition and professional interpretation of patient information. Recent studies have begun to investigate these technologies together. Potential applications include remote caries screening, preliminary assessment of periodontal and gingival conditions, oral-lesion triage, dental-trauma assessment, and prioritization of patients for professional evaluation. However, diagnostic performance under controlled conditions does not necessarily establish effectiveness in heterogeneous community populations. Image quality, dataset representativeness, algorithmic bias, false-positive and false-negative findings, privacy, cybersecurity, professional accountability, regulatory requirements, digital literacy, and unequal access to technology remain important considerations.

Conclusion: AI-enabled teledentistry may provide a useful adjunct to conventional dental services by supporting remote screening and prioritization. Its most appropriate near-term role may be human-supervised decision support rather than autonomous diagnosis. Clinical validation in diverse populations, standardized image-acquisition protocols, transparent governance, professional oversight, and integrated referral pathways are required before widespread implementation. A human-in-the-loop pathway connecting remote data acquisition, AI-assisted assessment, dentist verification, risk stratification, referral, definitive examination, and follow-up is proposed.

Keywords: Artificial Intelligence; Teledentistry; Dental AI; Oral Disease Screening; Dental Caries; Preventive Dentistry; Underserved Populations; Remote Dental Care; Clinical Decision Support; Health Equity

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