Generative AI (Gen-AI) in GBM models. (A) Preclinical data input: Readouts from preclinical models—including cell proliferation, cellular phenotypes, drug response, and survival metrics—are entered into the Gen-AI. (B) Multidimensional data integration: AI processes and integrates clinical and molecular data, including patient-specific clinical features (e.g., age, neurological symptoms, and imaging) and tumor-omics (genetics, epigenetics, proteomics, and metabolomics), along with insights from prior published studies and clinical trials. This integration allows the AI to categorize and decode complex model profiles. (C) Personalized treatment prediction and model optimization: Based on this comprehensive analysis, AI generates improved, personalized treatment options, linking preclinical model’s predictive drug response to real patient outcomes. The model’s accuracy is continually assessed, allowing for an iterative approach where outputs inform further model refinement, new screening strategies, and enhancements to therapeutic development. HTS, high-througput sequencing; MTS, medium-throughput sequencing; LTS, low-throughput sequencing. Created in BioRender.