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

Formative Evaluation of Digital Health Interventions (3627) Tools, Programs and Algorithms (431) Imaging Informatics (359) Machine Learning (2337) Data Science (410) Research Methodology - AI (56) Applications of AI (402)

Published on in Vol 10 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/91492, first published .
Futuristic MRI scanner with glowing interior and patient bed

Automated Machine Learning Frameworks for Radiomics: Comparative Evaluation Study

Automated Machine Learning Frameworks for Radiomics: Comparative Evaluation Study

Authors of this article:

Jose Lozano-Montoya1, 2 Author Orcid Image ;   Emilio Soria-Olivas3 Author Orcid Image ;   Almudena Fuster-Matanzo2 Author Orcid Image ;   Angel Alberich-Bayarri2 Author Orcid Image ;   Ana Jimenez-Pastor2 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Jose Lozano-Montoya   1, 2 , MSc ;   Emilio Soria-Olivas   3 , PhD ;   Almudena Fuster-Matanzo   2 , PhD ;   Angel Alberich-Bayarri   2 , PhD ;   Ana Jimenez-Pastor   2 , PhD

1 Universitat de València, Valencia, Valencia, Spain

2 Research & Frontiers in AI Department, Quantitative Imaging Biomarkers in Medicine, Valencia, Valencia, Spain

3 Intelligent Data Analysis Laboratory, Universitat de València, Valencia, Valencia, Spain

Corresponding Author:

  • Jose Lozano-Montoya, MSc
  • Research & Frontiers in AI Department
  • Quantitative Imaging Biomarkers in Medicine
  • Europa Building, Aragon Avenue, 30, 13th Floor
  • Valencia, Valencia 46021
  • Spain
  • Phone: 34 961243225
  • Email: joselozano@quibim.com