Value of artificial intelligence in neuro-oncology.
Authors
Affiliations (13)
Affiliations (13)
- Virtual Diagnostics Unit, QuantCo, Cambridge, MA, USA; Max Planck Institute for Biological Cybernetics, Systems Neuroscience Division, Tübingen, Germany.
- Division of Neuro-Oncology, Mass General Cancer Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Clinical Neurology and Neurological Surgery, University of California San Francisco, San Francisco, CA, USA.
- Division of Neuro-Oncology, Mass General Cancer Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Neurosurgery, Ludwig-Maximilians-University and University Hospital Munich, Munich, Germany.
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA.
- Department of Radiation Oncology, University of Michigan Hospital, Ann Arbor, MI, USA.
- Institute of Neuroscience and Medicine (INM-3, INM-4), Research Center Juelich (FZJ), Juelich, Germany; Department of Nuclear Medicine, RWTH Aachen University Hospital, Aachen, Germany.
- Institute of Neuroscience and Medicine (INM-3, INM-4), Research Center Juelich (FZJ), Juelich, Germany; Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
- Department of Pediatric Oncology, Dana-Farber Boston Children's Cancer and Blood Disorders Center, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Google DeepMind, Toronto, ON, Canada.
- Google Research, Mountain View, CA, USA.
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA.
- Division of Neuro-Oncology, Mass General Cancer Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Division of Neuro-Oncology, Mass General Cancer Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: [email protected].
Abstract
CNS cancers are complex, difficult-to-treat malignancies that remain insufficiently understood and mostly incurable, despite decades of research efforts. Artificial intelligence (AI) is poised to reshape neuro-oncological practice and research, driving advances in medical image analysis, neuro-molecular-genetic characterisation, biomarker discovery, therapeutic target identification, tailored management strategies, and neurorehabilitation. This Review examines key opportunities and challenges associated with AI applications along the neuro-oncological care trajectory. We highlight emerging trends in foundation models, biophysical modelling, synthetic data, and drug development and discuss regulatory, operational, and ethical hurdles across data, translation, and implementation gaps. Near-term clinical translation depends on scaling validated AI solutions for well defined clinical tasks. In contrast, more experimental AI solutions offer broader potential but require technical refinement and resolution of data and regulatory challenges. Addressing both general and neuro-oncology-specific issues is essential to unlock the full potential of AI and ensure its responsible, effective, and needs-based integration into neuro-oncological practice.