Artificial intelligence-enabled nanomedicine development: From rational design to manufacturing optimization

The translation of nanomedicines remains constrained by persistent bottlenecks, including complex design parameters, unpredictable in vivo behavior, and low diagnostic accuracy. Artificial intelligence (AI), with its advanced data processing and learning algorithms, offers new solutions to these challenges by learning from heterogeneous data and supporting decision-making throughout nanomedicine development. This review summarizes the AI methods and modeling workflows relevant to nanomedicine and surveys their applications in rational nanomaterial design, prediction of in vivo fate and nano-bio interactions, theranostic imaging analysis, and formulation and manufacturing optimization. This review further discusses the main barriers that currently limit translation, which largely concern data, model reliability, and computational cost. Future progress will depend on better data resources, more generalizable and interpretable models, and closer integration of AI with experimental and mechanistic approaches. We believe that through these advances, AI-enabled nanomedicine will ultimately achieve more reliable and translatable outcomes.