Purpose of review: This article explores the evolving role of artificial intelligence (AI) in neuro-oncology, highlighting its potential to enhance diagnostic accuracy, predict patient outcomes, optimize treatment planning, and streamline clinical workflows.
Recent findings: AI applications have led to significant advancements in automated tumor segmentation, molecular classification, risk stratification, treatment response evaluation, and computational pathology. AI-driven innovations have also accelerated drug discovery and leveraged natural language processing to generate structured clinical reports and extract actionable insights from unstructured data. AI has transformative potential in neuro-oncology; however, challenges like data quality, model generalizability, and clinical integration persist. Overcoming these barriers may involve new computational techniques and hardware efficiencies, as well as raising awareness, fostering interdisciplinary education, and expanding access to AI-driven tools.
Keywords: Artificial intelligence; Brain metastasis; Computational pathology; Deep learning; Glioma; Natural language processing.
© 2025. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
