Data infrastructures for AI in medical imaging: a report on the experiences of five EU projects

Artificial intelligence (AI) is transforming the field of medical imaging and has the potential to bring medicine from the era of ‘sick-care’ to the era of healthcare and prevention. The development of AI requires access to large, complete, and harmonized real-world datasets, representative of the p...

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Detalles Bibliográficos
Autores principales: Kondylakis, Haridimos, Kalokyri, Varvara, Sfakianakis, Stelios, Marias, Kostas, Tsiknakis, Manolis, Jimenez-Pastor, Ana, Camacho-Ramos, Eduardo, Blanquer, Ignacio, Segrelles, J. Damian, López-Huguet, Sergio, Barelle, Caroline, Kogut-Czarkowska, Magdalena, Tsakou, Gianna, Siopis, Nikolaos, Sakellariou, Zisis, Bizopoulos, Paschalis, Drossou, Vicky, Lalas, Antonios, Votis, Konstantinos, Mallol, Pedro, Marti-Bonmati, Luis, Alberich, Leonor Cerdá, Seymour, Karine, Boucher, Samuel, Ciarrocchi, Esther, Fromont, Lauren, Rambla, Jordi, Harms, Alexander, Gutierrez, Andrea, Starmans, Martijn P. A., Prior, Fred, Gelpi, Josep Ll., Lekadir, Karim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Vienna 2023
Materias:
Guideline/Position paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10164664/
https://www.ncbi.nlm.nih.gov/pubmed/37150779
http://dx.doi.org/10.1186/s41747-023-00336-x

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10164664/
https://www.ncbi.nlm.nih.gov/pubmed/37150779
http://dx.doi.org/10.1186/s41747-023-00336-x

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