Federated Learning in Dentistry: Chances and Challenges

Building performant and robust artificial intelligence (AI)–based applications for dentistry requires large and high-quality data sets, which usually reside in distributed data silos from multiple sources (e.g., different clinical institutes). Collaborative efforts are limited as privacy constraints...

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Detalles Bibliográficos
Autores principales: Rischke, R., Schneider, L., Müller, K., Samek, W., Schwendicke, F., Krois, J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2022
Materias:
Departments
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9516599/
https://www.ncbi.nlm.nih.gov/pubmed/35912725
http://dx.doi.org/10.1177/00220345221108953

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9516599/
https://www.ncbi.nlm.nih.gov/pubmed/35912725
http://dx.doi.org/10.1177/00220345221108953

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