MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering

TL;DR

MedMCQA is a comprehensive large-scale dataset of over 194,000 medical multiple-choice questions from entrance exams, designed to evaluate AI models' understanding across diverse medical topics and reasoning skills.

Contribution

The paper introduces MedMCQA, a novel, extensive dataset for medical question answering that covers a wide range of topics and reasoning challenges, facilitating advanced AI research in medical NLP.

Findings

High topical diversity in questions

Effective evaluation of reasoning abilities

Large-scale dataset for medical AI research

Abstract

This paper introduces MedMCQA, a new large-scale, Multiple-Choice Question Answering (MCQA) dataset designed to address real-world medical entrance exam questions. More than 194k high-quality AIIMS \& NEET PG entrance exam MCQs covering 2.4k healthcare topics and 21 medical subjects are collected with an average token length of 12.77 and high topical diversity. Each sample contains a question, correct answer(s), and other options which requires a deeper language understanding as it tests the 10+ reasoning abilities of a model across a wide range of medical subjects \& topics. A detailed explanation of the solution, along with the above information, is provided in this study.