  This paper outlines our submission to the MEDIQA2024 Multilingual and
Multimodal Medical Answer Generation (M3G) shared task. We report results for
two standalone solutions under the English category of the task, the first
involving two consecutive API calls to the Claude 3 Opus API and the second
involving training an image-disease label joint embedding in the style of CLIP
for image classification. These two solutions scored 1st and 2nd place
respectively on the competition leaderboard, substantially outperforming the
next best solution. Additionally, we discuss insights gained from
post-competition experiments. While the performance of these two solutions have
significant room for improvement due to the difficulty of the shared task and
the challenging nature of medical visual question answering in general, we
identify the multi-stage LLM approach and the CLIP image classification
approach as promising avenues for further investigation.
