Open Consortium for Decentralized Medical Artificial Intelligence

Informazioni relative al progetto

ODELIA

ID dell’accordo di sovvenzione: 101057091

DOI

10.3030/101057091

Data della firma CE 17 Novembre 2022

Data di avvio 1 Gennaio 2023

Data di completamento 31 Dicembre 2027

Finanziato da

• Health

Costo totale

€ 8 691 755,75

Contributo UE

€ 8 691 755,00

Coordinato da

EIBIR GEMEINNUTZIGE GMBH ZUR FORDERUNG DER ERFORSCHUNG DER BIOMEDIZINISCHEN BILDGEBUNG
Austria

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

Documents, reports (3)

Communication kit including website

Communication kit for ODELIA, including communication, dissemination and design assets such as a presentation template, standard slides introducing the project, a short project description, the logo and related visual assets, and the project website and online presence.

Midterm recruitment report

This report is due when 50% of the study population is recruited. The report includes an overview of the number of recruited participants by clinical sites, any problems in recruitment and, if applicable, a detailed description of implemented and planned measures to compensate for any incurred delays.

Instructions on training AI models with SL in a tangible demonstration case

Report with instructions on how to train AI models with SL based on a tangible demonstration case

Websites, patent fillings, videos etc. (2)

Project Video

First short video/animation to raise awareness of the ODELIA project

Manual on setting up SL onsite

Manual describing how to set up SL onsite

Pubblicazioni

Book chapters (1)

meval: A Statistical Toolbox for Fine-Grained Model Performance Analysis

Autori: Dishantkumar Sutariya, Eike Petersen
Pubblicato in: Lecture Notes in Computer Science, Fairness of AI in Medical Imaging, 2025
Editore: Springer Nature Switzerland
DOI: 10.1007/978-3-032-05870-6_19

Peer reviewed articles (10)

Swarm learning with weak supervision enables automatic breast cancer detection in magnetic resonance imaging

Autori: Oliver Lester Saldanha; Jiefu Zhu; Gustav Müller-Franzes; Zunamys I. Carrero; Nicholas R. Payne; Lorena Escudero Sánchez; Paul Christophe Varoutas; Sreenath Kyathanahally; Narmin Ghaffari Laleh; Kevin Pfeiffer; Marta Ligero; Jakob Behner; Kamarul A. Abdullah; Georgios Apostolakos; Chrysafoula Kolofousi; Antri Kleanthous; Michail Kalogeropoulos; Cristina Rossi; Sylwia Nowakowska; Alexandra Athanasiou; Raquel Perez-Lopez; Ritse Mann; Wouter Veldhuis; Julia Camps; Volkmar Schulz; Markus Wenzel; Sergey Morozov; Alexander Ciritsis; Christiane Kuhl; Fiona J. Gilbert; Daniel Truhn; Jakob Nikolas Kather
Pubblicato in: Communications Medicine, Numero 5, 38 (2025), 2025, ISSN 2730-664X
Editore: Springer Nature
DOI: 10.18154/RWTH-2025-10188

Large language models could make natural language again the universal interface of healthcare

Autori: Jakob Nikolas Kather; Dyke Ferber; Isabella C. Wiest; Stephen Gilbert; Daniel Truhn
Pubblicato in: Nature Medicine, 2024, ISSN 1546-170X
Editore: Springer Nature
DOI: 10.1038/S41591-024-03199-W

Overcoming regulatory barriers to the implementation of AI agents in healthcare

Autori: Oscar Freyer; Sanddhya Jayabalan; Jakob N. Kather; Stephen Gilbert
Pubblicato in: Nature Medicine, 2025, ISSN 1546-170X
Editore: Springer Nature
DOI: 10.1038/S41591-025-03841-1

Scientific Reports

Autori: Müller-Franzes, Gustav; Khader, Firas; Siepmann, Robert; Han, Tianyu; Kather, Jakob Nikolas; Nebelung, Sven; Truhn, Daniel
Pubblicato in: Scientific Reports, Numero (2025) 15:23979, 2025, ISSN 2045-2322
Editore: Springer Nature
DOI: 10.18154/RWTH-2025-06979

Reconstruction of patient-specific confounders in AI-based radiologic image interpretation using generative pretraining

Autori: Tianyu Han; Laura Zigutyte; Luisa Huck; Marc Huppertz; Robert Siepmann; Yossi Gandelsman; Christian Blüthgen; Firas Khader; Christiane Kuhl; Sven Nebelung; Jakob Nikolas Kather; Daniel Truhn
Pubblicato in: Cell Reports Medicine, 2024, ISSN 2666-3791
Editore: Cell Press (an imprint of Elsevier)
DOI: 10.48550/ARXIV.2309.17123

Nature Communications

Autori: Soroosh Tayebi Arasteh; Tianyu Han; Mahshad Lotfinia; Christiane Kuhl; Jakob Nikolas Kather; Daniel Truhn; Sven Nebelung
Pubblicato in: Nature Communications, Vol 15, Iss 1, Pp 1-12 (2024), 2024, ISSN 2041-1723
Editore: Nature Publishing Group
DOI: 10.48550/arxiv.2308.14120

Preserving fairness and diagnostic accuracy in private large-scale AI models for medical imaging

Autori: Soroosh Tayebi Arasteh; Alexander Ziller; Christiane Kuhl; Marcus Makowski; Sven Nebelung; Rickmer Braren; Daniel Rueckert; Daniel Truhn; Georgios Kaissis
Pubblicato in: Communications Medicine, 2024, ISSN 2730-664X
Editore: Springer Nature
DOI: 10.1038/S43856-024-00462-6

Scientific Reports

Autori: Firas Khader; Gustav Müller-Franzes; Soroosh Tayebi Arasteh; Tianyu Han; Christoph Haarburger; Maximilian Schulze-Hagen; Philipp Schad; Sandy Engelhardt; Bettina Baeßler; Sebastian Foersch; Johannes Stegmaier; Christiane Kuhl; Sven Nebelung; Jakob Nikolas Kather; Daniel Truhn
Pubblicato in: Scientific Reports, Vol 13, Iss 1, Pp 1-12 (2023), 2023, ISSN 2045-2322
Editore: Nature Publishing Group
DOI: 10.18154/rwth-2023-05893

Diffusion probabilistic versus generative adversarial models to reduce contrast agent dose in breast MRI

Autori: Gustav Müller-Franzes; Luisa Huck; Maike Bode; Sven Nebelung; Christiane Kuhl; Daniel Truhn; Teresa Lemainque
Pubblicato in: European Radiology Experimental, 2024, ISSN 2509-9280
Editore: SpringerOpen
DOI: 10.1186/S41747-024-00451-3

Nature Communications

Autori: Jan Clusmann; Dyke Ferber; Isabella C. Wiest; Carolin V. Schneider; Titus J. Brinker; Sebastian Foersch; Daniel Truhn; Jakob Nikolas Kather
Pubblicato in: Nature Communications, 2025, ISSN 2041-1723
Editore: Springer Nature
DOI: 10.1038/S41467-024-55631-X

Other (1)

A European Multi-Center Breast Cancer MRI Dataset

Autori: Gustav Müller-Franzes, Lorena Escudero Sánchez, Nicholas Payne, Alexandra Athanasiou, Michael Kalogeropoulos, Aitor Lopez, Alfredo Miguel Soro Busto, Julia Camps Herrero, Nika Rasoolzadeh, Tianyu Zhang, Ritse Mann, Debora Jutz, Maike Bode, et al
Pubblicato in: arXiv, Numero 2506.00474, ISSN 2331-8422
Editore: Cornell University