With the advantages afforded by automation and ML, researchers can focus more efforts on the aims and applications of a nanomedicine. One example would be more closely considering how a proposed nanomedicine interacts with unique patient biology. Overlooking this aspect is a contributing factor to the limited number of investigated formulations that successfully translate to the clinic based on the traditional development approach. To address this, it is important to design nanomedicines through a patient-focused lens, by identifying the disease application and understanding its specific pathophysiology. This also provides valuable insights into how a formulation can be optimized for a more personalized approach. Taking this into account, future nanomedicines can be transformative in the treatment and prevention of an even wider array of diseases. To realize the full potential of nanomedicines, we must begin to introduce and expand upon automation and AI in the pharmaceutical development pipeline. A) The number of publications on Web of Science between 2000-2021 corresponding to one of the search terms "lipid nanoparticle", "PLGA nanoparticle", or "polymer nanoparticle" and "cancer". The growing number of publications in these research areas over time suggests that there is interest to pursue such nanoformulations for cancer applications. B) A timeline of notable moments related to nanomedicine from 2000-2021 . Text boxes are coloured according to whether the product was approved by the FDA (purple), an agency other than the FDA (green), or involves a high-profile setback in clinical trials (blue). All liposomal formulations are non-PEGylated, unless otherwise noted. It is important to note that one cannot conclude certain trends in the number of publications for a given research area are caused by events listed in the timeline. Figure : Visual summary of study conducted by Wiercioch and Kirchmair which described a transfer learning approach to overcome a lack of data for the prediction of aqueous solubility of small molecules. Specifically, a deep learning architecture consisting of an encoder-decoder design was applied on a pKa dataset based on molecular structures (i.e., SMILES: simplified molecular input line entry system) for 6245 compounds. Then, transfer learning was used to predict solubility for a smaller, non-overlapping dataset (i.e., 1311 molecules) based on the pretrained pKa model. * Estimated prices were obtained from NeedyMeds.org, unless otherwise noted. The listed prices are rounded to the nearest whole number and reflect a range from the least to most expensive product when there are multiple dosage forms available.