  This paper introduces a new challenge and datasets to foster research toward
designing systems that can understand medical videos and provide visual answers
to natural language questions. We believe medical videos may provide the best
possible answers to many first aids, medical emergency, and medical education
questions. Toward this, we created the MedVidCL and MedVidQA datasets and
introduce the tasks of Medical Video Classification (MVC) and Medical Visual
Answer Localization (MVAL), two tasks that focus on cross-modal (medical
language and medical video) understanding. The proposed tasks and datasets have
the potential to support the development of sophisticated downstream
applications that can benefit the public and medical practitioners. Our
datasets consist of 6,117 annotated videos for the MVC task and 3,010 annotated
questions and answers timestamps from 899 videos for the MVAL task. These
datasets have been verified and corrected by medical informatics experts. We
have also benchmarked each task with the created MedVidCL and MedVidQA datasets
and proposed the multimodal learning methods that set competitive baselines for
future research.
