Polymers are ubiquitous to almost every aspect of modern society and their use in medical products is similarly pervasive. Despite this, the diversity in commercial polymers used in medicine is stunningly low. Considerable time and resources have been extended over the years towards the development of new polymeric biomaterials which address unmet needs left by the current generation of medical-grade polymers. Machine learning (ML) presents an unprecedented opportunity in this field to bypass the need for trial-and-error synthesis thus reducing the time and resources invested into new discoveries critical for advancing medical treatments. Current efforts pioneering applied ML in biomedical polymer design have overcome data availability limitations through combinatorial design and common property prediction algorithms. However, the lack of standardized characterization of biomedical parameters (e.g., degradation time, biocompatibility) represents a presently insurmountable obstacle to the inverse design of medical polymers. Herein, we identify a gap at the intersection of applied ML and biomedical polymer design as well as highlight current works at this junction more broadly.