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Diagnostic Support in Dentistry Through Artificial Intelligence: A Systematic Review.

Alessio Danilo Inchingolo1, Grazia Marinelli1, Arianna Fiore1

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Summary

Artificial intelligence (AI) shows promise in dental diagnostics, improving accuracy in areas like radiographic assessment and periodontal disease detection. Further research is needed to harmonize methods and validate AI tools across diverse patient groups for wider clinical use.

Keywords:
artificial intelligenceclinical decision-makingdentistrydiagnosisoral healthradiography

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Area of Science:

  • Dental Diagnostics
  • Artificial Intelligence in Healthcare
  • Medical Imaging Analysis

Background:

  • Artificial intelligence (AI) integration in dental diagnostics offers potential for enhanced precision, reproducibility, and accessibility.
  • The evolving landscape of AI in dentistry necessitates a thorough evaluation of its clinical performance.

Purpose of the Study:

  • To systematically review the clinical performance of AI-based diagnostic tools in dentistry.
  • To compare AI tools against traditional diagnostic methods across various dental specialties.
  • To identify AI applications in radiographic assessment, orthodontic classification, and periodontal disease detection.

Main Methods:

  • Systematic literature search of PubMed, Scopus, and Web of Science (January 2015 - June 2025).
  • Inclusion of English-language clinical studies on AI in dental diagnostics, adhering to PRISMA guidelines.
  • Quality appraisal and risk-of-bias assessment of 15 included studies.

Main Results:

  • AI systems demonstrated promising diagnostic capabilities in diverse dental fields.
  • Radiographic AI algorithms improved lesion detection and landmark identification.
  • Machine learning models effectively classified malocclusions and periodontal status; photographic analysis showed potential in geriatric and preventive care.
  • Methodological variability, small sample sizes, and lack of external validation limited generalizability.
  • Study quality was moderate to high, with some bias noted.

Conclusions:

  • AI shows significant promise as an adjunct tool in dental diagnostics, especially for imaging and decision support.
  • Wider clinical adoption requires methodological standardization and rigorous validation in diverse populations.
  • Further multicenter trials are essential to confirm the efficacy and generalizability of AI dental diagnostic tools.