Multimodal Question Answering in the Medical Domain: A summary of Existing Datasets and Systems

A large-scale (194k), Multiple-Choice Question Answering (MCQA) dataset designed to address realworld medical entrance exam questions.

[EMNLP'24] EHRAgent: Code Empowers Large Language Models for Complex Tabular Reasoning on Electronic Health Records

This project involves fine-tuning the T5 transformer model for medical question-answering tasks. The model is trained on a domain-specific dataset, enabling it to generate accurate and contextually relevant medical responses.