AI Is Reshaping Dentistry: What the Latest Research Reveals
How artificial intelligence is transforming diagnostics, treatment planning, and the future of dental care.
A New Era for Dentistry
Artificial intelligence (AI) is rapidly transforming industries worldwide and dentistry is no exception. Since large language models (LLMs) became widely accessible in 2022, the dental community has witnessed an acceleration in AI-assisted diagnostics, planning, and patient communication.
A new narrative review published in the International Dental Journal (2025) provides one of the most comprehensive overviews to date on how AI is being integrated into dentistry. This article (Part 1 of a two-part review) explores the foundations of AI and maps out its diverse, real-world applications across dental specialties.
Understanding the Foundations of AI in Dentistry
The authors begin by breaking down key AI concepts in a way that clinicians can relate to:
1. Narrow vs. General AI
Narrow AI, commonly used today, includes machine learning (ML), expert systems, and deep learning (DL).
General AI remains theoretical and is not yet used in dentistry.
2. Deep Learning and Neural Networks
DL models especially convolutional neural networks (CNNs) power many of dentistry’s image-based diagnostic tools. These algorithms excel at analyzing radiographs, photographs, cone-beam CT (CBCT) images, and more.
3. Large Language and Large Vision Models
LLMs (e.g., GPT models) support charting, documentation, patient education, and clinical decision-support.
LVMs handle image interpretation, from tooth segmentation to pathology detection.
4. Multimodal Models (MM)
These combine text, images, and clinical data, supporting comprehensive diagnosis and treatment planning.
How AI Is Being Used Across Dental Specialties
The review highlights that AI is already influencing nearly every branch of dentistry—and often with impressive accuracy.
1. Periodontics: More Accurate Diagnostics and Prognosis
AI assists in:
Detecting gingivitis and periodontitis from intraoral photos
Measuring radiographic bone loss with up to 85% accuracy
Predicting tooth extraction risks
Integrating patient factors (e.g., diabetes, smoking) for risk modeling
Classifying disease stages using LLMs
Meta-analyses show AI models reach 87% sensitivity and 84% accuracy in assessing alveolar bone loss.
2. Endodontics: Better Detection of Hidden Lesions
Using CBCT and periapical radiographs, AI can identify periapical lesions with diagnostic performance comparable or sometimes superior to clinicians.
Notable metrics:
93% detection accuracy for periapical lesions in CBCT
Automated pulp segmentation to support minimally invasive endodontics
AI-assisted identification of C-shaped canals
Chatbots (e.g., GPT-4) outperform earlier LLMs in pulpal diagnosis
3. Oral Medicine & Pathology: Earlier Cancer Detection
AI supports early identification of:
Oral cancer
Nasopharyngeal and laryngeal cancer
Oral potentially malignant disorders
CNNs have reached up to 97.5% accuracy in detecting oral cancer from clinical photographs.
AI can also analyze histopathology slides, improving diagnostic consistency and speed.
4. Restorative Dentistry & Prosthodontics: Smarter Design Tools
AI assists with:
Caries and vertical root fracture detection
Tooth wear evaluation
Automated tooth shade matching
Predicting the longevity of restorations
Designing crowns using GAN-based models
Automating dental charting on panoramic X-rays
AI-generated crowns show increasing realism, though human technicians still outperform AI in fine anatomy and morphology.
5. Pediatric Dentistry: Predicting Caries & Supporting Behavior Management
Applications include:
Detecting early childhood caries with up to 97% accuracy
Caries risk prediction using ML models
Detecting plaque via smartphone photos
AI-assisted parent education using LLMs
Personalized, gamified behavioral management tools
6. Forensic Odontology: Faster and More Reliable Identification
AI is emerging as a powerful tool for:
Age estimation using panoramic radiographs (95%+ accuracy in younger patients)
Sex determination from craniofacial metrics
Bite mark analysis
Matching pre- and post-mortem data with CNNs (up to 100% accuracy in some models)
7. Oral & Maxillofacial Surgery: Enhanced Planning and Robotics
AI contributes to:
Predicting surgical difficulty (e.g., impacted third molars)
Orthognathic surgery planning
Implant planning with reduced time
Robotic-assisted implant placement with sub-millimeter accuracy
Improved visualization for surgical decision-making
8. Orthodontics: Landmark Detection & Treatment Prediction
Machine learning models can:
Detect cephalometric landmarks within 1–2 mm precision
Classify crossbites with 98% accuracy
Predict extraction decisions (up to 91% accuracy)
9. Orofacial Pain & Sleep Medicine: Supporting Complex Diagnoses
AI assists in diagnosing:
Temporomandibular disorders (TMD)
Neuropathic facial pain
Sleep apnea (accuracy up to 91%)
Bruxism through movement data analysis
LLMs may soon help streamline differential diagnosis and triage.
Looking Ahead: Opportunities and Challenges
The authors emphasize that while AI is reshaping dentistry, successful adoption requires:
Rigorous validation
High-quality datasets
Ethical guidelines
Regulatory frameworks
Interdisciplinary collaboration
Continuous clinician oversight
AI is not replacing dentists but it is becoming a powerful co-pilot that enhances precision, efficiency, and patient outcomes.
Conclusion
This review clearly shows that AI is no longer a future concept it is an active force across dental disciplines. From diagnostics and treatment planning to patient education and forensic analysis, AI offers transformative potential.
Part 2 of the article (not covered here) will address AI’s role in patient education, ethics, and guidelines for its responsible use.
Original Article Reference
Samaranayake L, Tuygunov N, Schwendicke F, et al. The Transformative Role of Artificial Intelligence in Dentistry: A Comprehensive Overview. Part 1: Fundamentals of AI, and its Contemporary Applications in Dentistry. International Dental Journal, 2025.