Electronic medical records (EMRs) contain sensitive and detailed documentation on a variety of conditions at the individual level. Because EMRs are subject to confidentiality requirements, access to them is limited. In an attempt to address privacy limitations, knowledge-driven experimental artificially generated electronic medical records (EMRBots) have been introduced. EMRBot repositories have been used in a variety of scenarios to advance teaching, enhance student dissertations, facilitate hackathons, and produce R packages. In addition to describing its methodology, the manuscript reviews EMRBot use cases published by independent researchers.