Artificial Intelligence in Dental Medicine

This module provides an overview of the fundamental concepts of artificial intelligence (AI), including a brief historical background to contextualize its current relevance in dentistry. Participants are introduced to machine learning (ML) and its distinguishing features compared to more traditional statistical approaches and the human clinical reasoning process. The module also outlines key modeling approaches through which AI is applied within clinical dentistry, dental education, and research.

Based on the ubiquitous nature of imaging in dentistry, computer vision is arguably the most relevant AI application in the field. In this deep dive module, participants are introduced to the technical background of computer vision, including deep learning and neural networks. Real-world applications are explored through demonstrations of FDA-cleared algorithms, offering participants a clear view of current capabilities and limitations. The module is complemented by a workshop guiding participants through the first steps of developing their own computer vision projects, as well as a case study showcasing commercial computer vision solutions currently available on the market.

This module explores the integration of AI in precision dentistry, focusing on the development and application of prognostic modeling for patient-specific treatment planning. Participants will gain insight into AI-driven risk assessments, predictive analytics, and their role in improving long-term treatment outcomes. Key topics include the fundamentals of data collection, training predictive models, and ethical considerations in AI implementation. Participants will learn how prognostic models can enhance clinical decision-making, addressing challenges in dental treatment planning and enabling personalized care pathways.

This module focuses on the technological background and real-world applications of generative AI in dentistry, with a particular emphasis on patient communication and dental education. Participants will gain an understanding of the underlying principles (e.g., auto-regressive prediction) used in generative AI. They will further explore how large language models (LLM) and text-to-image models can enhance patient interactions and clinical workflows while addressing potential pitfalls, including hallucinations. A case study by a Harvard research group complements this module, demonstrating generative AI’s potential to revolutionize teaching and learning in dental medicine. 

The aim of this module is to inspire the participants’ understanding of AI to go beyond replicating human diagnostics and uncover hidden insights. Participants will explore innovative new approaches in clinical dentistry, oral and maxillofacial surgery, as well as translational biomedical research. Participants will engage with ethical and philosophical considerations, examining the impact on medical responsibility, intellectual property, and the evolving role of clinicians. The module also addresses the broader implications of AI’s integration into decision-making processes, challenging traditional boundaries of human expertise in clinical practice.