Survey of Multimodal Medical Question Answering

Abstract

Multimodal medical question answering (MMQA) is a vital area bridging healthcare and Artificial Intelligence (AI). This survey methodically examines the MMQA research published in recent years. We collect academic literature through Google Scholar, applying bibliometric analysis to the publications and datasets used in these studies. Our analysis uncovers the increasing interest in MMQA over time, with diverse domains such as natural language processing, computer vision, and large language models contributing to the research. The AI methods used in multimodal question answering in the medical domain are a prominent focus, accompanied by applicability of MMQA to the medical field. MMQA in the medical field has its unique challenges due to the sensitive nature of medicine as a science dealing with human health. The survey reveals MMQA research to be in an exploratory stage, discussing different methods, datasets, and potential business models. Future research is expected to focus on application development by big tech companies, such as MedPalm. The survey aims to provide insights into the current state of multimodal medical question answering, highlighting the growing interest from academia and industry. The identified research gaps and trends will guide future investigations and encourage collaborative efforts to advance this transformative field.

Domain-Specific Few-Shot Table Prompt Question Answering via Contrastive Exemplar Selection

“…It aims to translate natural language questions and corresponding table information into SQL statements through the appropriate design of algorithms or models. Due to its ability to intelligently retrieve and analyze structured data, it can be widely applied in various fields such as finance, power, and bridge maintenance, enhancing their levels of digitization and intelligence [2][3][4].…”