Artificial Intelligence in Oncology Drug Development and Management: A Precision Medicine Perspective

QingWei Yang^1*Caixia Fang²Pengfa Zhou²Xuerong Zhang²Yongsheng He²

• ¹Department of Laboratory, Qingyang People's Hospital, Qingyang, China
• ²Qingyang People's Hospital, Qingyang, Gansu Province, China

The management of oncology drugs is inherently complex, facing challenges such as high development costs, prolonged timelines, and substantial inter-patient heterogeneity. Recent advances in artificial intelligence (AI) have introduced transformative capabilities across the entire cancer drug lifecycle—from target discovery and compound screening to clinical trial optimization, individualized therapy, toxicity management, supply chain logistics, and regulatory oversight. AI enables precise target identification, accelerates virtual drug screening and molecular design, and enhances clinical trial efficiency through intelligent patient stratification and adaptive protocols. Moreover, AI facilitates personalized treatment decision-making, early prediction of drug resistance, and real-time toxicity surveillance, while improving pharmacovigilance and post-market drug evaluation using real-world data. Here, "post-market drug evaluation" refers to real-world effectiveness and safety assessment using spontaneous reports (e.g., FAERS/VigiBase) and EHR/claims-based outcomes, rather than cost-effectiveness analyses. Examples include EHR-NLP to surface immune-related adverse events, AI-assisted trial recruitment and adaptive designs, and individualized dosing frameworks (e.g., CURATE.AI). Despite its enormous promise, AI-driven oncology drug management faces notable challenges in data integration, model interpretability, clinical translation, fairness, and regulatory governance. This review comprehensively summarizes the current applications of AI in oncology pharmacology, highlights key opportunities and barriers, and explores future directions at the intersection of AI, precision medicine, and cancer therapeutics. Future priorities include prospective multi-site evaluations, fairness auditing, and continuous post-market algorithmovigilance.