AI in Neuro-Oncology: An Overarching Review

Artificial intelligence (AI) is increasingly being applied across neuro-oncology domains to analyze complex, high-dimensional, multimodal data. Despite major advances in the biological understanding and classification of CNS tumors, clinical outcomes for many of them remain poor, reflecting intra- and inter-tumor heterogeneity and limitations of conventional reductionist approaches. AI offers a paradigm shift to capture this heterogeneity and provide quantitative, reproducible diagnostic, predictive, and prognostic insights. This article describes a joint effort across the Response Assessment in Neuro-Oncology (RANO) working groups to provide an overarching review of the AI use across six key domains of neuro-oncology: neuroimaging, neuropathology, laboratory medicine, neurosurgery, drug discovery, and clinical trials. Applications closest to clinical use include imaging segmentation and quantification, AI-assisted image reconstruction, DNA methylation-based classification, and workflow optimization. Radiogenomics, WSI-based molecular prediction, AI-guided surgical targeting, liquid biopsy interpretation, AI-guided drug discovery, digital twins, and in silico trials remain promising but require stronger prospective validation. Key challenges include limited availability of large diverse datasets, variability in data acquisition and reference standards, and limited external validation. This review provides a unifying framework for understanding the role of AI across neuro-oncology and highlights key considerations for its responsible implementation, emphasizing standardization, reproducibility, multi-institutional validation, and human oversight.