When biased data are used for research, the results may reflect the same biases if appropriate precautions are not taken. In this systematic review, researchers describe possible types of bias (e.g., implicit, selection) that can result from research with artificial intelligence (AI) using electronic health record (EHR) data. Along with recommendations to reduce introducing bias into the data model, the authors stress the importance of standardized reporting of model development and real-world testing.