Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.
Machine learning in neuroradiology: Recent developments and applications
Manos Siderakis, Department of Radiology, Bioiatriki Healthcare Group, Kifisia 115 26, Attikí, Greece
Georgios Velonakis, Nikolaos-Achilleas Arkoudis, Research Unit of Radiology and Medical Imaging, National and Kapodistrian University of Athens, Athens 115 28, Attikí, Greece
Georgios Velonakis, Nikolaos-Achilleas Arkoudis, 2nd Department of Radiology, Attikon University General Hospital, National and Kapodistrian University of Athens, Athens 124 62, Attikí, Greece
Author contributions: Siderakis M, Velonakis G, and Arkoudis NA contributed substantially to the conceptualization, design and preparation of the review and drafting of the manuscript; Velonakis G and Arkoudis NA provided supervision; and all authors reviewed and approved the final manuscript.
AI contribution statement: AI tools (GPT-5.6), were used solely for language polishing and formatting assistance. No AI tool was used to generate research data, interpret results, or formulate conclusions. All AI-assisted content was critically reviewed and revised by the authors, who take full responsibility for the accuracy, originality, and integrity of the manuscript.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Nikolaos-Achilleas Arkoudis, MD, PhD, Lecturer, Researcher, Research Unit of Radiology and Medical Imaging, National and Kapodistrian University of Athens, Papadiamantopoulou 19, Athens 115 28, Attikí, Greece. nick.arkoudis@gmail.com
Received: May 7, 2026
Revised: July 20, 2026
Accepted: July 30, 2026
Published online: August 28, 2026
Processing time: 114 Days and 3.5 Hours
Revised: July 20, 2026
Accepted: July 30, 2026
Published online: August 28, 2026
Processing time: 114 Days and 3.5 Hours
Core Tip
Core Tip: In this article, we focus on the most recent (post 2023) developments and applications of machine learning (ML) in neuroradiology. The ML models presented here attempt to assist radiologists in stroke imaging, cerebral aneurysm and hemorrhage detection, neuro-oncology, spine imaging, and demyelinating and degenerative diseases. Despite the potential of ML to become an essential player in modern neuroradiology departments, the complexity of ML, especially deep learning models, remains an obstacle to their broad clinical application. The framework of explainable artificial intelligence attempts to address this issue and increase transparency in the model structure and decision-making process.