Clinical Applications of AI in Contemporary Dentistry

Clinical Applications of AI in Contemporary Dentistry

Category: Digital Dentistry

Instructor(s): Rata Rokhshad

Course structure: Lessons: 8 lessons | Duration: 2 h 33 min

Course overview

Artificial intelligence is rapidly moving from research into everyday dental practice, supporting clinicians across diagnostics, treatment planning, monitoring and digital workflows.

This course provides a practical, evidence-informed overview of how AI is currently being validated and used across dental specialties, with a focus on real clinical applications, available tools and the evidence behind them.

What you will learn

• AI for caries detection and radiographic diagnosis.
• AI-assisted periodontal assessment, disease classification and personalized homecare recommendations.
• AI applications in endodontic diagnosis, root canal anatomy analysis and treatment planning.
• AI in orthodontics, including cephalometric analysis, treatment planning and extraction decision support.
• AI applications in pediatric dentistry, oral cancer screening and oral potentially malignant disorder screening.
• AI in prosthodontics, implant planning, digital smile design, intraoral scanning and CAD/CAM workflows.
• Prognostic modeling and predictive analytics for treatment outcomes.
• FDA-cleared dental AI tools, their validated indications and current clinical applications.

Course lessons

Lesson 1: AI and the FDA Regulatory Landscape

• FDA regulatory pathways for AI/ML medical devices: 510(k) clearance, De Novo classification and PMA approval.
• The regulatory pathway most commonly used by dental AI/ML products.
• Current landscape of FDA-cleared dental AI/ML products and the growth of the field.
• What FDA clearance means for a dental AI product and what it does not guarantee about performance in individual clinical practice.
• Clinical validation, intended use and the importance of independent evaluation before integrating AI tools into practice.

Lesson 2: AI in Prosthodontics, Digital Scanning, and CAD/CAM

• AI-assisted implant position planning based on CBCT data.
• Automated segmentation of anatomical structures relevant to surgical planning, including the mandibular canal.
• AI-assisted intraoral scanning and AI-supported CAD/CAM workflows for crowns, dentures and digital smile design.
• Clinical applications where AI can reduce chair time and areas where manual correction and clinician oversight remain necessary.

Lesson 3: Oral Cancer and Mucosal Lesion Screening

• Computer vision approaches for screening oral potentially malignant disorders (OPMD) using clinical photographs.
• Current evidence on AI-based screening: promising sensitivity in controlled studies but limited real-world validation.
• Clinical limitations of AI screening and the importance of specialist referral and biopsy when indicated.
• Why a positive or negative AI screening result cannot replace definitive diagnosis by biopsy.

Lesson 4: AI in Pediatric Dentistry

• Current evidence for AI in pediatric dental diagnostics, including caries detection and growth assessment.
• Limitations of adult-trained AI models in pediatric and mixed-dentition cases.
• Emerging AI applications for monitoring myofunctional therapy compliance, orthodontic treatment and airway health in children.
• Pediatric-specific considerations for informed consent and communication with minor patients and caregivers.
• A pediatric-specific checklist for the critical appraisal of AI tools before clinical use.

Lesson 5: AI in Orthodontics: Cephalometric Analysis and Treatment Planning

• Automated cephalometric landmark identification and reported accuracy compared with manual tracing.
• AI-assisted decision support for extraction versus non-extraction treatment.
• Prognostic modeling and predictive analytics for treatment duration and outcomes.
• Digital smile design and AI-assisted aligner treatment planning.

Lesson 6: AI Applications in Endodontics

• AI-assisted detection and classification of periapical lesions and apical periodontitis on dental radiographs and CBCT.
• AI-supported analysis of root canal anatomy, including curved, calcified and complex canal systems.
• Automated working length estimation and AI-assisted assessment of root canal morphology.
• Detection and segmentation of endodontic findings, including missed canals, periapical pathology and vertical root fractures.
• AI-assisted treatment planning and decision support in primary and retreatment cases.
• Current evidence, limitations and clinical validation of AI tools in endodontics.
• Where AI can support clinical decision-making and where independent clinician assessment remains essential.

Lesson 7: AI for Periodontal Classification and Personalized Homecare

• AI-assisted measurement of alveolar bone loss on dental radiographs.
• AI-based classification and staging of periodontal disease.
• Personalized homecare and oral hygiene recommendations based on the patient’s periodontal risk profile.
• Detection and segmentation of periapical lesions and apical periodontitis.
• AI-assisted detection of vertical root fractures.
• Current clinical role of AI tools as decision-support systems rather than replacements for professional diagnosis.

Lesson 8: AI for Caries Detection and Radiographic Diagnosis

• Deep learning for caries detection on bitewing and periapical radiographs.
• Typical reported sensitivity ranges for AI-based caries detection.
• FDA-cleared AI solutions for caries detection, including Pearl, Overjet and VideaHealth.
• Indications and findings each AI solution is cleared to flag.
• Current evidence and limitations, including risk of bias in published caries-detection studies.
• Clinical interpretation of AI-generated findings and the importance of independent clinician review of the radiograph.

Who is this course for?

This course is designed for dentists and dental professionals who want a clearer, evidence-informed understanding of dental AI and its practical applications across specialties.

Explore the current role of AI in dentistry and build a more informed approach to digital clinical workflows.