Exploring Diffusion Models for Oral Health Applications: A Conceptual Review
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, highlig...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11104086/ |
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| author | Mohsen Tabejamaat Amira Soliman |
| author_facet | Mohsen Tabejamaat Amira Soliman |
| author_sort | Mohsen Tabejamaat |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-59a8750c9d3047f791d2fe2b6e31c3d5 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-59a8750c9d3047f791d2fe2b6e31c3d52025-08-20T03:43:55ZengIEEEIEEE Access2169-35362025-01-011313525413527210.1109/ACCESS.2025.359393311104086Exploring Diffusion Models for Oral Health Applications: A Conceptual ReviewMohsen Tabejamaat0https://orcid.org/0000-0002-8815-5730Amira Soliman1https://orcid.org/0000-0002-0264-8762Center for Applied Intelligent Systems Research (CAISR), Halmstad University, Halmstad, SwedenCenter for Applied Intelligent Systems Research (CAISR), Halmstad University, Halmstad, SwedenRecent 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.https://ieeexplore.ieee.org/document/11104086/Oral health applicationsdiffusion modelsdeep learning for medical applications |
| spellingShingle | Mohsen Tabejamaat Amira Soliman Exploring Diffusion Models for Oral Health Applications: A Conceptual Review IEEE Access Oral health applications diffusion models deep learning for medical applications |
| title | Exploring Diffusion Models for Oral Health Applications: A Conceptual Review |
| title_full | Exploring Diffusion Models for Oral Health Applications: A Conceptual Review |
| title_fullStr | Exploring Diffusion Models for Oral Health Applications: A Conceptual Review |
| title_full_unstemmed | Exploring Diffusion Models for Oral Health Applications: A Conceptual Review |
| title_short | Exploring Diffusion Models for Oral Health Applications: A Conceptual Review |
| title_sort | exploring diffusion models for oral health applications a conceptual review |
| topic | Oral health applications diffusion models deep learning for medical applications |
| url | https://ieeexplore.ieee.org/document/11104086/ |
| work_keys_str_mv | AT mohsentabejamaat exploringdiffusionmodelsfororalhealthapplicationsaconceptualreview AT amirasoliman exploringdiffusionmodelsfororalhealthapplicationsaconceptualreview |