ABSTRACT

Despite maximal safe surgical resection, radiation, and temozolomide-based chemotherapy, glioblastoma (GBM) is still the most aggressive primary malignant brain tumor in adults, with a median survival of about 15–20 months. The immunosuppressive tumor microenvironment, fast treatment resistance, infiltrative growth pattern, and amazing genetic heterogeneity continue to pose challenges to researchers and clinicians throughout the world. Artificial intelligence (AI) has become a game-changing technology in recent years. It has the potential to completely change every aspect of managing glioblastoma, from early diagnosis and molecular classification to treatment planning, drug discovery, prognosis prediction, and individualized therapeutic decision-making. Precision neuro-oncology is becoming more and more possible thanks to deep learning, machine learning, radiomics, genomics, and multi-omics integration, which reveal clinically significant patterns that are frequently missed by traditional analytical techniques. Additionally, AI-driven advancements are speeding up the identification of new therapeutic targets, refining immunotherapeutic approaches, enhancing surgical navigation, and enabling adaptive therapy monitoring. AI is one of the most promising paths for precision medicine in glioblastoma, despite the fact that there are still many obstacles to overcome, such as data standardization, algorithm openness, regulatory approval, and ethical issues. This editorial addresses the latest developments in AI technologies that are changing the way glioblastoma is treated, outlines the successes and drawbacks of the current state of the field, and offers suggestions for future lines of inquiry.