Generative artificial intelligence: synthetic datasets in dentistry
Introduction Artificial Intelligence (AI) algorithms, particularly Deep Learning (DL) models are known to be data intensive. This has increased the demand for digital data in all domains of healthcare, including dentistry. The main hindrance in the progress of AI is access to diverse datasets which...
Uloženo v:
| Vydáno v: | BDJ open Ročník 10; číslo 1; s. 13 - 5 |
|---|---|
| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
London
Nature Publishing Group UK
01.03.2024
Springer Nature B.V Nature Publishing Group |
| Témata: | |
| ISSN: | 2056-807X, 2056-807X |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Shrnutí: | Introduction
Artificial Intelligence (AI) algorithms, particularly Deep Learning (DL) models are known to be data intensive. This has increased the demand for digital data in all domains of healthcare, including dentistry. The main hindrance in the progress of AI is access to diverse datasets which train DL models ensuring optimal performance, comparable to subject experts. However, administration of these traditionally acquired datasets is challenging due to privacy regulations and the extensive manual annotation required by subject experts. Biases such as ethical, socioeconomic and class imbalances are also incorporated during the curation of these datasets, limiting their overall generalizability. These challenges prevent their accrual at a larger scale for training DL models.
Methods
Generative AI techniques can be useful in the production of Synthetic Datasets (SDs) that can overcome issues affecting traditionally acquired datasets. Variational autoencoders, generative adversarial networks and diffusion models have been used to generate SDs. The following text is a review of these generative AI techniques and their operations. It discusses the chances of SDs and challenges with potential solutions which will improve the understanding of healthcare professionals working in AI research.
Conclusion
Synthetic data customized to the need of researchers can be produced to train robust AI models. These models, having been trained on such a diverse dataset will be applicable for dissemination across countries. However, there is a need for the limitations associated with SDs to be better understood, and attempts made to overcome those concerns prior to their widespread use. |
|---|---|
| Bibliografie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Review-3 content type line 23 |
| ISSN: | 2056-807X 2056-807X |
| DOI: | 10.1038/s41405-024-00198-4 |