  The increase in the availability of online videos has transformed the way we
access information and knowledge. A growing number of individuals now prefer
instructional videos as they offer a series of step-by-step procedures to
accomplish particular tasks. The instructional videos from the medical domain
may provide the best possible visual answers to first aid, medical emergency,
and medical education questions. Toward this, this paper is focused on
answering health-related questions asked by the public by providing visual
answers from medical videos. The scarcity of large-scale datasets in the
medical domain is a key challenge that hinders the development of applications
that can help the public with their health-related questions. To address this
issue, we first proposed a pipelined approach to create two large-scale
datasets: HealthVidQA-CRF and HealthVidQA-Prompt. Later, we proposed monomodal
and multimodal approaches that can effectively provide visual answers from
medical videos to natural language questions. We conducted a comprehensive
analysis of the results, focusing on the impact of the created datasets on
model training and the significance of visual features in enhancing the
performance of the monomodal and multi-modal approaches. Our findings suggest
that these datasets have the potential to enhance the performance of medical
visual answer localization tasks and provide a promising future direction to
further enhance the performance by using pre-trained language-vision models.
