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

Development and Evaluation of Research Methods, Instruments and Tools (774) Artificial Intelligence (2249) Big Data (313) Digital Science, Open Science (92)

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 (8) 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
  4. Nguyen T, Elmaleh D. LizAI XT—AI-Accelerated Management Platform for Complex Healthcare Data at Scale, Beyond EMR/EHR and Dashboards. Big Data and Cognitive Computing 2025;9(11):275 View
  5. Zeng Q, Huang C, Zhu J. AI-driven transformation of precision medicine: a comprehensive narrative review of key application areas, emerging paradigms, and future directions. Frontiers in Public Health 2026;13 View

Books/Policy Documents

  1. Jain D, Bhati N, Bhardwaj N. Federated Learning for Smart Agriculture and Food Quality Enhancement. View

Conference Proceedings

  1. Yon S, Espinoza J, Alvarado C. 2025 IEEE 16th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON). Federated XGBoost-Powered Web Application for Safeguarding Personal Health Data in Hospitals and Clinics of Metropolitan Lima View
  2. Faes A, Pirmani A, Moreau Y, Peeters L. 2025 3rd International Conference on Federated Learning Technologies and Applications (FLTA). Applying Federated Learning to Block-Term Tensor Regression for Decentralised Data Analysis of Biomedical Data View