Abstract
Artificial intelligence is expanding the clinical value of intraoral scanning beyond documentation and treatment planning. By analyzing scan data and highlighting potential oral health findings, AI-assisted tools can support clinicians in identifying surface and proximal caries, plaque, gingival recession, and tooth wear.
This lecture explores how visual overlays, structured assessments, and longitudinal scan comparisons can contribute to more consistent clinical evaluations and data-informed treatment decisions. Sequential scans may help clinicians monitor changes over time, assess disease progression, evaluate treatment outcomes, and communicate the importance of preventive care.
The presentation will also demonstrate how complex clinical information can be transformed into clear and accessible visual explanations for patients. Patient-facing digital tools can reinforce conversations in the dental practice, improve understanding, and encourage greater participation in treatment and prevention.
Through clinical examples and practical workflows, participants will gain insight into how AI-assisted intraoral analysis can enhance diagnostic confidence, strengthen patient communication, and support a more preventive, personalized, and patient-centered approach to oral healthcare. The lecture will also address the importance of responsible implementation, data quality, and continued clinical oversight when incorporating AI into everyday practice.
Learning Objectives
- Understand the foundations of clinical AI: Explain how artificial intelligence can be trained on large datasets of intraoral scans to support consistent identification of oral health findings.
- Recognize AI-assisted clinical findings: Explore the identification and visualization of surface caries, proximal caries, plaque, gingival recession, and tooth wear.
- Apply longitudinal monitoring: Understand how sequential intraoral scans can be compared to monitor changes, disease progression, and treatment outcomes over time.
- Strengthen patient communication: Use intuitive visual overlays and patient-facing digital tools to explain clinical findings and encourage greater patient understanding, engagement, and participation.
- Integrate AI responsibly into clinical practice: Recognize AI as a decision-support tool that complements, rather than replaces, professional clinical judgment.
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