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Area of Science:

  • Dental informatics
  • Artificial intelligence in healthcare
  • Machine learning applications

Background:

  • Artificial intelligence (AI) involves machines performing human-like tasks, with machine learning (ML) and deep learning as key subsets.
  • AI applications in dentistry encompass diagnostics, treatment planning, and research, promising efficiency and personalized care.

Purpose of the Study:

  • To review the applications, limitations, and future potential of AI in dentistry.
  • To highlight the benefits of AI in streamlining dental workflows and improving patient outcomes.
  • To address the barriers to AI integration in routine dental practice.

Main Methods:

  • Narrative review of AI applications in dental diagnostics, treatment planning, and research.
  • Discussion of challenges including data availability, methodological rigor, and ethical considerations.
  • Exploration of future directions such as explainable AI and federated learning.

Main Results:

  • AI can enhance dental care quality, efficiency, and accessibility, leading to personalized, preventive dentistry.
  • Significant barriers to AI adoption include limited data, lack of standards, and unresolved ethical/practical questions.
  • Demonstrating tangible value and ensuring trustworthiness are crucial for AI implementation.

Conclusions:

  • AI holds transformative potential for dentistry, but successful integration requires addressing data, methodological, and ethical hurdles.
  • Future dental AI must prioritize patient privacy, trustworthiness, and generalizability, supported by human oversight and evidence-based standards.
  • Dental education must evolve to equip the future workforce with the digital literacy needed for AI-driven practices.