AI in Dentistry: Innovations, Ethical Considerations, and Integration Barriers

Area of Science:

• Dentistry
• Artificial Intelligence
• Oral Medicine

Background:

• Artificial Intelligence (AI), including machine learning (ML) and deep learning (DL), is increasingly adopted in dentistry.
• AI tools aim to improve diagnostic accuracy, treatment planning, and workflow efficiency.
• Challenges such as ethical, legal, and technological barriers hinder AI acceptance in oral medicine.

Purpose of the Study:

• To review current applications of AI in oral medicine.
• To discuss emerging AI technologies and their development.
• To identify and analyze barriers to AI integration in dental practices.

Main Methods:

• A narrative review of peer-reviewed articles from major databases (PubMed, Scopus, Web of Science, Google Scholar).
• Focus on AI applications in diagnostic imaging, predictive analysis, real-time documentation, and workflow automation over the last decade.
• Examination of AI model improvements and critical impediments like ethical concerns and integration challenges.

Main Results:

• AI demonstrates high accuracy in radiographic diagnostics (e.g., CBCT scans, intraoral photographs).
• AI-driven predictive analysis supports personalized care and disease prevention.
• Workflow automation, utilizing tools like TensorFlow, reduces patient record processing time by over 30%.

Conclusions:

• AI significantly advances dentistry through improved diagnostics, planning, and administrative automation.
• Ethical and legislative frameworks must evolve to support responsible AI integration.
• Future research should focus on AI interpretability, standardized protocols, and interdisciplinary collaboration to maximize AI's potential in dentistry.