# Medical Exam Question Answering with Large-scale Reading Comprehension

arXiv: https://arxiv.org/abs/1802.10279 · HTML: https://mlsanity.com/paper/1802.10279/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2018.

## Built on by (most cited first, 29 of 29)

- [MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering](https://mlsanity.com/paper/2203.14371/) (2022)
- [Biomedical Question Answering: A Survey of Approaches and Challenges](https://mlsanity.com/paper/2102.05281/) (2021)
- [Apollo: A Lightweight Multilingual Medical LLM towards Democratizing Medical AI to 6B People](https://mlsanity.com/paper/2403.03640/) (2024)
- [Few shot chain-of-thought driven reasoning to prompt LLMs for open ended medical question answering](https://mlsanity.com/paper/2403.04890/) (2024)
- [HealthQ: Unveiling Questioning Capabilities of LLM Chains in Healthcare Conversations](https://mlsanity.com/paper/2409.19487/) (2024)
- [English Machine Reading Comprehension Datasets: A Survey](https://mlsanity.com/paper/2101.10421/) (2021)
- [Towards Consistent Natural-Language Explanations via Explanation-Consistency Finetuning](https://mlsanity.com/paper/2401.13986/) (2024)
- [Mix-of-Granularity: Optimize the Chunking Granularity for Retrieval-Augmented Generation](https://mlsanity.com/paper/2406.00456/) (2024)
- [Efficiently Democratizing Medical LLMs for 50 Languages via a Mixture of Language Family Experts](https://mlsanity.com/paper/2410.10626/) (2024)
- [Medchain: Bridging the Gap Between LLM Agents and Clinical Practice with Interactive Sequence](https://mlsanity.com/paper/2412.01605/) (2024)
- [Q-Pain: A Question Answering Dataset to Measure Social Bias in Pain Management](https://mlsanity.com/paper/2108.01764/) (2021)
- [Augmenting Black-box LLMs with Medical Textbooks for Biomedical Question Answering](https://mlsanity.com/paper/2309.02233/) (2023)
- [Is GPT-OSS Good? A Comprehensive Evaluation of OpenAI's Latest Open Source Models](https://mlsanity.com/paper/2508.12461/) (2025)
- [Large Language Models Leverage External Knowledge to Extend Clinical Insight Beyond Language Boundaries](https://mlsanity.com/paper/2305.10163/) (2023)
- [ExplainCPE: A Free-text Explanation Benchmark of Chinese Pharmacist Examination](https://mlsanity.com/paper/2305.12945/) (2023)
- [Me LLaMA: Foundation Large Language Models for Medical Applications](https://mlsanity.com/paper/2402.12749/) (2024)
- [MeDiaQA: A Question Answering Dataset on Medical Dialogues](https://mlsanity.com/paper/2108.08074/) (2021)
- [MedEthicEval: Evaluating Large Language Models Based on Chinese Medical Ethics](https://mlsanity.com/paper/2503.02374/) (2025)
- [Question Answering for Electronic Health Records: A Scoping Review of datasets and models](https://mlsanity.com/paper/2310.08759/) (2023)
- [MING-MOE: Enhancing Medical Multi-Task Learning in Large Language Models with Sparse Mixture of Low-Rank Adapter Experts](https://mlsanity.com/paper/2404.09027/) (2024)
- [SPBERTQA: A Two-Stage Question Answering System Based on Sentence Transformers for Medical Texts](https://mlsanity.com/paper/2206.09600/) (2022)
- [Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory](https://mlsanity.com/paper/2507.18178/) (2025)
- [MedCare: Advancing Medical LLMs through Decoupling Clinical Alignment and Knowledge Aggregation](https://mlsanity.com/paper/2406.17484/) (2024)
- [Evaluating and Enhancing Large Language Models Performance in Domain-specific Medicine: Osteoarthritis Management with DocOA](https://mlsanity.com/paper/2401.12998/) (2024)
- [SciPredict: Can LLMs Predict the Outcomes of Scientific Experiments in Natural Sciences?](https://mlsanity.com/paper/2604.10718/) (2026)
- [Med-CoDE: Medical Critique based Disagreement Evaluation Framework](https://mlsanity.com/paper/2504.15330/) (2025)
- [Cross-Lingual Empirical Evaluation of Large Language Models for Arabic Medical Tasks](https://mlsanity.com/paper/2602.05374/) (2026)
- [Can GPT Redefine Medical Understanding? Evaluating GPT on Biomedical Machine Reading Comprehension](https://mlsanity.com/paper/2405.18682/) (2024)
- [EchoQA: A Large Collection of Instruction Tuning Data for Echocardiogram Reports](https://mlsanity.com/paper/2503.02365/) (2025)

## Abstract (arXiv)

Reading and understanding text is one important component in computer aided diagnosis in clinical medicine, also being a major research problem in the field of NLP. In this work, we introduce a question-answering task called MedQA to study answering questions in clinical medicine using knowledge in a large-scale document collection. The aim of MedQA is to answer real-world questions with large-scale reading comprehension. We propose our solution SeaReader--a modular end-to-end reading comprehension model based on LSTM networks and dual-path attention architecture. The novel dual-path attention models information flow from two perspectives and has the ability to simultaneously read individual documents and integrate information across multiple documents. In experiments our SeaReader achieved a large increase in accuracy on MedQA over competing models. Additionally, we develop a series of novel techniques to demonstrate the interpretation of the question answering process in SeaReader.