Artificial intelligence–guided personalization of pharmacological treatment in neurological disorders, deep learning models for drug optimization and outcome prediction in brain tumor management

Abstract

Precision neurology is moving from population-average prescribing toward individual response forecasting, driven by three converging data streams: mechanistic biomarkers (genomics and molecular pathology), high-density physiological time series (EEG, ECG, wearable signals), and quantitative imaging (MRI, PET, perfusion). In neuro-oncology, the 2021 WHO CNS tumor framework tightened the bond between diagnosis and molecular features, which in turn reshapes therapeutic stratification and outcome modeling. In parallel, validated pharmacogenetic guidance (notably HLA and CYP variants) already demonstrates how a small set of genotypes can deterministically alter drug choice and dosing in seizure management. This article proposes a clinically realistic AI architecture that fuses guideline-grade pharmacogenomics with deep learning derived imaging phenotypes and longitudinal outcomes, emphasizing calibration, uncertainty quantification, and safety constraints. We assemble only publicly verifiable datasets and consensus-grade clinical guidance to outline a reproducible pathway for building and auditing models that predict drug benefit, adverse events, and tumor control trajectories without overstating algorithmic certainty.