Nanomedicine harnesses nanoscale materials, such as lipid, polymeric, and inorganic nanoparticles, to deliver diagnostic or therapeutic agents for cancer, infectious disease, and neurological disorders, among others. However, translating promising nanoparticle designs into clinically approved products remains a challenge. Factors such as particle size, surface chemistry, and payload interactions must be optimized, and preclinical results often fail to predict human efficacy. In recent years, artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools to address these hurdles at every stage of nanomedicine development. By rapidly screening extensive libraries and extracting structure-function relationships, AI-driven models can rationalize nanoparticle formulation, predict biodistribution, and guide optimal design. Techniques like high-throughput DNA barcoding and automated liquid handling facilitate robust, large-scale data collection, feeding into computational pipelines that expedite discovery while reducing reliance on resource-intensive trial-and-error experiments. AI-based platforms also enable improved modeling of protein corona formation, which profoundly affects nanoparticle immunogenicity and cellular uptake. Despite these advances, challenges persist in data standardization, model generalizability, and establishing a clear regulatory framework since no dedicated U.S. Food and Drug Administration (FDA) guidance addresses the intersection of AI and nanomedicine. Overcoming these limitations requires harmonized data sharing, rigorous in vivo validation, and clear ethical and regulatory guidelines. This review summarizes the rapidly evolving landscape of AI in nanomedicine, highlighting key successes in design and preclinical prediction, as well as persistent obstacles to full-scale clinical integration. By illuminating these dynamics, we aim to chart a more efficient path forward in developing next-generation nanomedicine.
Chou et al. (2025) studied this question.