Digital dentistry has witnessed significant advancements in recent years, driven by extensive research following the introduction of cutting-edge technologies such as CAD/CAM and 3D oral scanners. Until now, 2D images obtained via x-ray or CT scans were critical to detect anomalies and for decision-making. This review describes the main principles and applications of supervised, unsupervised, and reinforcement learning in medical applications. In this context, we present a diverse range of artificial intelligence networks with potential applications in dentistry, accompanied by existing results in the field.

Introduction
Supervised Learning in MedicalApplications
    1. Image analysis and interpretation
    2. Disease prediction and risk stratification
Unsupervised Learning Approaches inMedical Research
    1. Clustering is used when grouping data
    2. Feature extraction and dimensionality reduction
    3. Data augmentation
Reinforcement Learning in PersonalizedTreatment and Decision-Making
    1. Personalized treatment plans
    2. Clinical decision support systems
Challenges and Future Directions
    1. Data privacy and ethical considerations
    2. Interpretable models and explainability
    3. Future directions and integration
Conclusions
Funding
Conflicts of Interest
References

• Yoohyun Lee(Department of Oral Microbiology, School of Dentistry, Chonnam National University, Gwangju 61186, Republic of Korea)
• Seung-Ho Ohk(Department of Oral Microbiology, School of Dentistry, Chonnam National University, Gwangju 61186, Republic of Korea) Corresponding Author