In summary, AI and ML are experiencing resounding growth in nanomedicine applications, albeit in the early stages (Table ). In these cases, ML algorithms can guide formulation and production, and predict design parameters for desired nanomedicines . It is well-recognized that the limiting factor in successful and generalizable implementations of AI and ML in nanomedicine development is data, or the lack thereof . This is particularly relevant for data-hungry techniques, such as neural networks . In its current form, AI and ML techniques are mainly applied to literature-mined data, but there is a need to consolidate nanomedicine information through curated databases. While there are several nanomaterial databases currently online , it is expected that continued establishment of comprehensive databases will support "big data" approaches for the data-driven development of nanomedicines. In this way, more complex applications currently being explored, including prediction of pharmacokinetic and pharmacodynamic parameters, potential toxicity, and biological interactions, can be refined and improved .