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Background <p>Dental caries is the most prevalent chronic, noncommunicable condition affecting individuals of all ages and socio-economic status. The recent technological advancements in artificial intelligence (AI), digital diagnostics, and teledentistry have been genuinely promising in revolutionizing the future of early caries detection and preventive care. However, an integrated understanding of these advancements and their clinical readiness remains limited.</p> Aim <p>To systematically map and synthesize the current evidence on the use of AI, digital diagnostic technologies, and teledentistry for the early diagnosis in dental caries.</p> Method <p>This scoping review followed the Arksey and O’Malley framework and adhered to PRISMA-ScR guidelines. Studies published between 1997 and 2025 were identified through PubMed, Scopus, Web of Science, and manual searches. Articles with AI, digital diagnostic tools, or teledentistry for caries detection were selected, with a specific focus on early caries detection. Data extraction was performed using a standardized charting form and narration across three topics: AI-assisted diagnostics, digital tools, and remote detection through teledentistry.</p> Results <p>Thirty studies were considered after screening and evaluation of eligibility as they met the selection criteria out of 1000 initial records. The studies included retrospective (n = 10), prospective (n = 7), diagnostic accuracy (n = 6), in-vitro (n = 5), and feasibility studies (n = 2). AI-supported studies showed excellent diagnostic accuracy ranging from well over 90% for the more performing AI to sensitivity and specificity values of 80–95%. However, digital methods, including near-infrared light transillumination, laser fluorescence, photothermal imaging, and ultrasonic technology yielded mixed but positive results in early lesion identification.</p> Conclusion <p>This scoping review highlights the increasing role of AI, digital diagnostics, and teledentistry in the early detection of dental caries. These technologies augment diagnostic precision, improve preventive care, and provide greater access, particularly for underserved areas. However, regarding real-world validation, standardization and ethical integration remain challenges. Future work needed in clinical trials, data quality, and regulatory harmonization to support safe, effective, and equitable implementation in dental practice.</p>

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Emerging trends in the early diagnosis of dental caries: a scoping review of artificial intelligence, digital diagnostics, and teledentistry

  • 期刊论文

  • 发表日期：

    2026年2月6日

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  • Syed Nahid Basheer,
  • Arwa Ali Daghrery,
  • Nassreen H. Albar,
  • Syed Wali Peeran,
  • Mohmed Isaqali Karobari

摘要

Background

Dental caries is the most prevalent chronic, noncommunicable condition affecting individuals of all ages and socio-economic status. The recent technological advancements in artificial intelligence (AI), digital diagnostics, and teledentistry have been genuinely promising in revolutionizing the future of early caries detection and preventive care. However, an integrated understanding of these advancements and their clinical readiness remains limited.

Aim

To systematically map and synthesize the current evidence on the use of AI, digital diagnostic technologies, and teledentistry for the early diagnosis in dental caries.

Method

This scoping review followed the Arksey and O’Malley framework and adhered to PRISMA-ScR guidelines. Studies published between 1997 and 2025 were identified through PubMed, Scopus, Web of Science, and manual searches. Articles with AI, digital diagnostic tools, or teledentistry for caries detection were selected, with a specific focus on early caries detection. Data extraction was performed using a standardized charting form and narration across three topics: AI-assisted diagnostics, digital tools, and remote detection through teledentistry.

Results

Thirty studies were considered after screening and evaluation of eligibility as they met the selection criteria out of 1000 initial records. The studies included retrospective (n = 10), prospective (n = 7), diagnostic accuracy (n = 6), in-vitro (n = 5), and feasibility studies (n = 2). AI-supported studies showed excellent diagnostic accuracy ranging from well over 90% for the more performing AI to sensitivity and specificity values of 80–95%. However, digital methods, including near-infrared light transillumination, laser fluorescence, photothermal imaging, and ultrasonic technology yielded mixed but positive results in early lesion identification.

Conclusion

This scoping review highlights the increasing role of AI, digital diagnostics, and teledentistry in the early detection of dental caries. These technologies augment diagnostic precision, improve preventive care, and provide greater access, particularly for underserved areas. However, regarding real-world validation, standardization and ethical integration remain challenges. Future work needed in clinical trials, data quality, and regulatory harmonization to support safe, effective, and equitable implementation in dental practice.