Exploring Diffusion Models for Oral Health Applications: A Conceptual Review - Mohsen Tabejamaat; Amira Soliman (IEEE Access)
Recent advances in generative modeling have revolutionized data synthesis, simplifying the creation of realistic data and addressing challenges related to data scarcity and intensive data collection. This review provides a comprehensive overview of the use of diffusion models in oral health, highlighting their effectiveness in dental therapy and care, especially given the complexities of data collection and alignment in this field. We explore the potential of diffusion models for robust learning with noisy and misaligned data, such as panoramic X-rays, PET and CT scans, and Photoacoustic data. We also discuss how diffusion models go beyond data generation to support multiple tasks, a critical requirement for practical machine-learning applications in the medical domain. This includes their integration with other machine learning techniques, such as object detection and representation learning, which necessitates significant structural innovations in diffusion models, a topic we explore in depth in this review. Lastly, we identify current challenges and propose future research directions for these applications, highlighting novel areas for further study. This review aims to support the advancement of diffusion modeling in dental data science and facilitate its practical application to real-world challenges in the field.