Systematic Review of Artificial Intelligence in Conservative Dentistry and Endodontics: Applications and Future Prospects

Abstract

Aim: To systematically review the current applications, diagnostic accuracy, challenges, and future direction of artificial intelligence (AI) in Conservative Dentistry and Endodontics, adhering to PRISMA guidelines.

Methods: Comprehensive searches were conducted in PubMed, Scopus, Web of Science, Cochrane Library, and Google Scholar for studies on AI, machine learning, or deep learning in Conservative Dentistry or Endodontics. Duplicates were removed, eligible articles screened and quality appraised.

Results: Thirty studies were included, covering AI’s applications in caries detection, shade selection, margin identification, restoration assessment, workflow optimization, root canal morphology analysis, working length determination, periapical lesion/root fracture detection, prognosis, and educational tools. Most studies report AI diagnostic accuracy ranging from 89% to 98%, often rivalling or surpassing human examiners. Challenges include data quality, algorithm transparency, integration, ethical and regulatory concerns.

Conclusions: AI demonstrates high potential to revolutionize Conservative Dentistry and Endodontics, with evidence for improved diagnostic consistency, efficiency, and patient outcomes. Further prospective studies, standardized datasets, and ethical frameworks are essential for routine adoption.