Glioma represents a dominant primary intracranial malignancy in the central nervous
system. Artificial intelligence that mainly includes machine learning, and deep learning
computational approaches, presents a unique opportunity to enhance clinical management
of glioma through improving tumor segmentation, diagnosis, differentiation, grading,
treatment, prediction of clinical outcomes (prognosis, and recurrence), molecular
features, clinical classification, characterization of the tumor microenvironment,
and drug discovery. A growing body of recent studies apply artificial intelligence-based
models to disparate data sources of glioma, covering imaging modalities, digital pathology,
high-throughput multi-omics data (especially emerging single-cell RNA sequencing and
spatial transcriptome), etc. While these early findings are promising, future studies
are required to normalize artificial intelligence-based models to improve the generalizability
and interpretability of the results. Despite prominent issues, targeted clinical application
of artificial intelligence approaches in glioma will facilitate the development of
precision medicine of this field. If these challenges can be overcome, artificial
intelligence has the potential to profoundly change the way patients with or at risk
of glioma are provided with more rational care.