A Comprehensive Review of Artificial Intelligence for Brain Tumor Analysis: Taxonomy, Robustness, and Open Challenges in Neuro-Oncology.
Authors
Affiliations (5)
Affiliations (5)
- Department of Data Science and Artificial Intelligence, Amman Arab University, Amman 11953, Jordan.
- Department of Computer Science, Amman Arab University, Amman 11953, Jordan.
- Research & Innovation Division, Rabdan Academy, Abu Dhabi 114646, United Arab Emirates.
- English Department, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia.
- Basic Science Department, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia.
Abstract
Detecting brain tumors can be challenging as a clinical problem because of tumor heterogeneity and reliance on manual neuroimaging interpretation, which can be prone to human error. Artificial intelligence (AI) has shown strong potential as a clinical decision-support tool, assisting radiologists in improving diagnostic accuracy and supporting the interpretation of neuroimaging data. AI using machine learning (ML) and deep learning (DL) algorithms has performed credibly in tumor detection, segmentation, and classification tasks. Challenges such as dataset bias, limited generalization, lack of explainability, and high computational costs must be addressed before clinical application. This article provides a comprehensive review of AI methods applied to brain tumor imaging, with a primary focus on adult diffuse gliomas and secondary coverage of brain metastases, meningiomas, and pediatric tumors where relevant. The major contribution of this review is a new three-factor (diagnostic tasks, learning strategies, and data modalities) taxonomy. Beyond accuracy-based metrics, we provide a qualitative assessment of robustness, generalization, and the principal barriers to clinical adoption identified in the published literature, while acknowledging that comprehensive clinical utility evidence remains an open research direction.