Translating digital PCT findings that leverage AI/ML into improved health outcomes for all patients rests on the use of well-managed and representative datasets. Both over- and underrepresentation in AI/ML training datasets are problematic for downstream uses and can lead to patient harms. Investigators should understand how algorithmic biases can enter the AI/ML training pipeline and develop practical ways correct them.