AI-Driven Precision Medicine: Comprehensive Applications in Disease Prediction, Personalized Treatment, and Drug Discovery

Abstract

This review explores AI’s transformative role in precision medicine, focusing on disease prediction, personalized treatment, and drug discovery. In disease prediction, AI uses EHRs, imaging, and multi-omics data to stratify risks: XGBoost outperforms traditional models in CVD risk prediction; deep learning enhances earlycancer detection (e.g., oral cancer via histopathology images); multi-omics integration aids neurodegenerative disease forecasting; and GCNs predict infectious outbreaks via real-time keyword analysis. For personalized treatment, AI tailors strategies: it analyzes genomic profiles to guide cancer therapy (e.g., identifying HER2 activation in CDK4/6i-resistant breast cancer); PK/PD modeling optimizes drug dosages (e.g., rituximab in nephropathy); it refines clinical trial patient selection (e.g., ASM choice for epilepsy); improves mental health diagnosis/treatment; and designs personalized stroke rehabilitation via wearable sensor data. In drug discovery, AI accelerates the pipeline: it identifies targets (e.g., SSO binding sites in triple-negative breast cancer); virtual screening (e.g., DeepDock for JAK3 inhibitors) and de novo design (e.g., CLMs for PI3Kγ inhibitors) find lead compounds; MIFAM-DTI predicts drug-target interactions; AI optimizes clinical trial design; and it enables drug repurposing (e.g., identifying fibrosis-related drugs via EHRs). Key challenges include data privacy (addressed via blockchain/SecPri-BGMPOP), algorithmic bias (needing diverse datasets), explainable AI (critical for CDSS trust), and multi-omics integration. AI-driven precision medicine promises proactive, personalized healthcare, requiring collaboration across stakeholders for ethical implementation.