This paper is in the following e-collection/theme issue:

Development and Evaluation of Research Methods, Instruments and Tools (680) Artificial Intelligence (1888) Big Data (301) Digital Science, Open Science (87)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/55496, first published .
Accessible Ecosystem for Clinical Research (Federated Learning for Everyone): Development and Usability Study

Accessible Ecosystem for Clinical Research (Federated Learning for Everyone): Development and Usability Study

Accessible Ecosystem for Clinical Research (Federated Learning for Everyone): Development and Usability Study

Authors of this article:

Ashkan Pirmani1, 2, 3, 4 Author Orcid Image ;   Martijn Oldenhof1 Author Orcid Image ;   Liesbet M Peeters2, 3, 4 Author Orcid Image ;   Edward De Brouwer1 Author Orcid Image ;   Yves Moreau1 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

Journals

  1. Resende C, Abreu M, Presa Ramos J, Carda J, Costa L, Cardoso F, Pereira D, Teixeira E, Tonin F, Duarte-Ramos F. Addressing Challenges to Enhance Clinical Research in Portugal: Insights from the OncoT3 Expert Group Delphi Study. Cureus 2024 View
  2. Sarani Rad F, Li J. Privacy-Preserving Glycemic Management in Type 1 Diabetes: Development and Validation of a Multiobjective Federated Reinforcement Learning Framework. JMIR Diabetes 2025;10:e72874 View
  3. Pirmani A, De Brouwer E, Arany Á, Oldenhof M, Passemiers A, Faes A, Kalincik T, Ozakbas S, Gouider R, Willekens B, Horakova D, Havrdova E, Patti F, Prat A, Lugaresi A, Tomassini V, Grammond P, Cartechini E, Roos I, Boz C, Alroughani R, Amato M, Buzzard K, Lechner-Scott J, Guimarães J, Solaro C, Gerlach O, Soysal A, Kuhle J, Sanchez-Menoyo J, Spitaleri D, Csepany T, Van Wijmeersch B, Ampapa R, Prevost J, Khoury S, Van Pesch V, John N, Maimone D, Weinstock-Guttman B, Laureys G, McCombe P, Blanco Y, Altintas A, Al-Asmi A, Garber J, Van der Walt A, Butzkueven H, de Gans K, Rozsa C, Taylor B, Al-Harbi T, Sas A, Rajda C, Gray O, Decoo D, Carroll W, Kermode A, Fabis-Pedrini M, Mason D, Perez-Sempere A, Simu M, Shuey N, Singhal B, Cauchi M, Hardy T, Ramanathan S, Lalive P, Sirbu C, Hughes S, Castillo Trivino T, Peeters L, Moreau Y. Personalized federated learning for predicting disability progression in multiple sclerosis using real-world routine clinical data. npj Digital Medicine 2025;8(1) View