The MedVidCL dataset contains a collection of 6, 617 videos annotated into ‘medical instructional’, ‘medical non-instructional' and ‘non-medical’ classes. A two-step approach is used to construct the MedVidCL dataset. In the first step, the videos annotated by health informatics experts are used to train a machine learning model that predicts the given video to one of the three aforementioned classes. In the second step, only the high-confidence videos are used and health informatics experts assess the model’s predicted video category and update the category wherever needed.