Artificial Intelligence in Precision Medicine and Patient-Specific Drug Design
Sweksha Ranjan, Arpita Singh, Ruchi Yadav · Biomedical & Pharmacology Journal · 2025
Artificial intelligence (AI) has emerged as a transformative force in personalized healthcare and precision medicine over the past decade. AI techniques like machine learning, deep learning, and natural language processing make possible the study of huge quantities of heterogeneous patient records from electronic health records, genomic profiles, wearable devices, and clinical trials. This allows for more accurate disease prediction, personalized treatment planning, and tailored drug discovery. Key areas of impact include AI-driven biomarker discovery, virtual drug screening, de novo drug design, and pharmacogenomics. The integration of AI is revolutionizing multiple aspects of precision medicine, from identifying novel therapeutic targets to optimizing clinical trial design and drug dosing. AI algorithms can detect subtle patterns in complex biological data, predict drug-target interactions, and simulate molecular behaviour to accelerate the typically costly and time-consuming drug development process. However, challenges remain around data quality, privacy, algorithmic bias, and equitable implementation. Ethical considerations regarding genetic discrimination and informed consent also need to be carefully addressed. This review examines the current applications, challenges, and future directions of AI in advancing patient-specific therapies and drug development.