AI Integration in Glioma Diagnosis and Treatment: Progress in Imagingand Genomic Applications

Introduction: This review explores the role of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), in enhancing molecular diagnostics and therapeutic strategies for glioma. Method: A comprehensive literature review was conducted using prominent scientific databases to assess current applications of AI, ML, and DL in glioma diagnosis and treatment. The review focused on medical imaging, pathology, gene expression analysis, and personalized medicine. Results: AI techniques, especially DL, effectively analyze complex datasets such as medical imaging and genomic profiles, improving diagnostic precision and efficiency. Integration of AI in microarray technologies facilitates the identification of glioma-associated biomarkers and supports disease classification and prognosis. Models such as artificial neural networks (ANNs) and Bayesian frameworks enhance the interpretation of gene expression data and the extraction of disease-specific signatures. Discussion: This review highlights AI’s potential in glioma care by improving imaging and genomic analysis, enabling biomarker identification, and advancing personalized treatment. It also emphasizes the importance of clinically validated and interpretable AI systems. Conclusion: AI has transformative potential in glioma care, offering improved diagnostic accuracy and enabling personalized therapeutic strategies. Continued research into AIdriven models is essential to advance precision medicine and elevate the standard of glioma healthcare