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

KGARevion: AI Agent for Knowledge-Intensive Biomedical QA

🧠💊 MediMind: Medical Question Answering System powered by BioBERT + Flan-T5. Intelligent routing for 8 question types, confidence-based abstention, 200k+ medical Q&A pairs. React frontend + FastAPI backend.

This project presents a medical question–answering language model built by fine-tuning Google Gemma-2-2B-IT using LoRA (Low-Rank Adaptation) 🧠⚕️. The primary objective is to adapt a general-purpose large language model to the healthcare domain in a parameter-efficient, reproducible, and resource-aware manner.

Instruction-tuned LLaMA 2 chatbot fine-tuned with LoRA on real medical Q&A data. Built for conversational health-related queries using Transformers and PEFT.

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.

MedRAFT: A Chinese medical question answering system using Retrieval-Augmented Generation (RAG) and Retrieval-Augmented Fine-Tuning (RAFT) with knowledge distillation for efficient deployment.

CMPE 252 final project by Ravikumar Komandur Narayanan on PEFT methods for domain-specific QA.