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

Digital Health Reviews (3456) Clinical Information and Decision Making (3454) Decision Support for Health Professionals (2074) Imaging Informatics (432) Artificial Intelligence (4454) Precision Medicine (416) Computer-Aided Diagnosis (401) Cancer Prognosis Models and Machine Learning (128)

Published on in Vol 28 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/78377, first published .
Medical team monitors brainwave activity on a computer screen showing a 3D brain model.

The Performance of Artificial Intelligence in Classifying Molecular Markers in Adult-Type Gliomas Using Histopathological Images: Systematic Review

The Performance of Artificial Intelligence in Classifying Molecular Markers in Adult-Type Gliomas Using Histopathological Images: Systematic Review

Authors of this article:

Obada Almaabreh1 Author Orcid Image ;   Rukaya Al-Dafi1 Author Orcid Image ;   Aliya Tabassum2 Author Orcid Image ;   Ahmad Othman1 Author Orcid Image ;   Alaa Abd-alrazaq3, 4 Author Orcid Image
Article Authors Cited by (1) Tweetations (8) Metrics

Journals

  1. Uthra B, Sivashankari R. Adaptive Gated Multimodal Fusion for Explainable and Uncertainty-Aware Tumor Grade Assessment. IEEE Access 2026;14:87392 View