# EHRXQA: A Multi-Modal Question Answering Dataset for Electronic Health Records with Chest X-ray Images

arXiv: https://arxiv.org/abs/2310.18652 · HTML: https://mlsanity.com/paper/2310.18652/

## Key facts

- Cited by 53 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 62 references that are in the map.
- First posted on arXiv 28 Oct 2023.

## Builds on (most cited first, 30 of 33)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering](https://mlsanity.com/paper/2209.09513/) (2022)
- [CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning](https://mlsanity.com/paper/1612.06890/) (2016)
- [Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://mlsanity.com/paper/1809.08887/) (2018)
- [Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs](https://mlsanity.com/paper/2305.03111/) (2023)
- [Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning](https://mlsanity.com/paper/1709.00103/) (2017)
- [Capabilities of GPT-4 on Medical Challenge Problems](https://mlsanity.com/paper/2303.13375/) (2023)
- [SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical Visual Question Answering](https://mlsanity.com/paper/2102.09542/) (2021)
- [Binding Language Models in Symbolic Languages](https://mlsanity.com/paper/2210.02875/) (2022)
- [MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Text](https://mlsanity.com/paper/2210.02928/) (2022)
- [MultiModalQA: Complex Question Answering over Text, Tables and Images](https://mlsanity.com/paper/2104.06039/) (2021)
- [emrQA: A Large Corpus for Question Answering on Electronic Medical Records](https://mlsanity.com/paper/1809.00732/) (2018)
- [MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data](https://mlsanity.com/paper/2206.01347/) (2022)
- [WebQA: Multihop and Multimodal QA](https://mlsanity.com/paper/2109.00590/) (2021)
- [Multi-Modal Masked Autoencoders for Medical Vision-and-Language Pre-Training](https://mlsanity.com/paper/2209.07098/) (2022)
- [Multi-modal Understanding and Generation for Medical Images and Text via Vision-Language Pre-Training](https://mlsanity.com/paper/2105.11333/) (2021)
- [Medical Visual Question Answering: A Survey](https://mlsanity.com/paper/2111.10056/) (2021)
- [Chest ImaGenome Dataset for Clinical Reasoning](https://mlsanity.com/paper/2108.00316/) (2021)
- [Text-to-SQL Generation for Question Answering on Electronic Medical Records](https://mlsanity.com/paper/1908.01839/) (2019)
- [VQA-LOL: Visual Question Answering under the Lens of Logic](https://mlsanity.com/paper/2002.08325/) (2020)
- [EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Records](https://mlsanity.com/paper/2301.07695/) (2023)
- [ManyModalQA: Modality Disambiguation and QA over Diverse Inputs](https://mlsanity.com/paper/2001.08034/) (2020)
- [Conversational Question Answering on Heterogeneous Sources](https://mlsanity.com/paper/2204.11677/) (2022)
- [DrugEHRQA: A Question Answering Dataset on Structured and Unstructured Electronic Health Records For Medicine Related Queries](https://mlsanity.com/paper/2205.01290/) (2022)
- [Interpretable Medical Image Visual Question Answering via Multi-Modal Relationship Graph Learning](https://mlsanity.com/paper/2302.09636/) (2023)
- [Learning to Ask Like a Physician](https://mlsanity.com/paper/2206.02696/) (2022)
- [Question Answering for Complex Electronic Health Records Database using Unified Encoder-Decoder Architecture](https://mlsanity.com/paper/2111.14703/) (2021)

## Built on by (most cited first, 30 of 53)

- [MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making](https://mlsanity.com/paper/2404.15155/) (2024)
- [Enhanced Contrastive Learning with Multi-view Longitudinal Data for Chest X-ray Report Generation](https://mlsanity.com/paper/2502.20056/) (2025)
- [CLIP in Medical Imaging: A Survey](https://mlsanity.com/paper/2312.07353/) (2023)
- [MedicalAgentsBench for Complex Medical Reasoning: Comparing Internalized Reasoning Models versus Externalized Agent-based Frameworks](https://mlsanity.com/paper/2503.07459/) (2025)
- [EHRNoteQA: An LLM Benchmark for Real-World Clinical Practice Using Discharge Summaries](https://mlsanity.com/paper/2402.16040/) (2024)
- [GEMeX: A Large-Scale, Groundable, and Explainable Medical VQA Benchmark for Chest X-ray Diagnosis](https://mlsanity.com/paper/2411.16778/) (2024)
- [Libra: Leveraging Temporal Images for Biomedical Radiology Analysis](https://mlsanity.com/paper/2411.19378/) (2024)
- [ReXVQA: A Large-scale Visual Question Answering Benchmark for Generalist Chest X-ray Understanding](https://mlsanity.com/paper/2506.04353/) (2025)
- [Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks](https://mlsanity.com/paper/2410.02331/) (2024)
- [M4CXR: Exploring Multi-task Potentials of Multi-modal Large Language Models for Chest X-ray Interpretation](https://mlsanity.com/paper/2408.16213/) (2024)
- [SM3-Text-to-Query: Synthetic Multi-Model Medical Text-to-Query Benchmark](https://mlsanity.com/paper/2411.05521/) (2024)
- [MAP: Evaluation and Multi-Agent Enhancement of Large Language Models for Inpatient Pathways](https://mlsanity.com/paper/2503.13205/) (2025)
- [MedOrch: Medical Diagnosis with Tool-Augmented Reasoning Agents for Flexible Extensibility](https://mlsanity.com/paper/2506.00235/) (2025)
- [Enhancing Human-Computer Interaction in Chest X-ray Analysis using Vision and Language Model with Eye Gaze Patterns](https://mlsanity.com/paper/2404.02370/) (2024)
- [AOR: Anatomical Ontology-Guided Reasoning for Medical Large Multimodal Model in Chest X-Ray Interpretation](https://mlsanity.com/paper/2505.02830/) (2025)
- [Citrus-V: Advancing Medical Foundation Models with Unified Medical Image Grounding for Clinical Reasoning](https://mlsanity.com/paper/2509.19090/) (2025)
- [Memory-Augmented Multimodal LLMs for Surgical VQA via Self-Contained Inquiry](https://mlsanity.com/paper/2411.10937/) (2024)
- [Overconfidence and Calibration in Medical VQA: Empirical Findings and Hallucination-Aware Mitigation](https://mlsanity.com/paper/2604.02543/) (2026)
- [SilVar-Med: A Speech-Driven Visual Language Model for Explainable Abnormality Detection in Medical Imaging](https://mlsanity.com/paper/2504.10642/) (2025)
- [MOTOR: Multimodal Optimal Transport via Grounded Retrieval in Medical Visual Question Answering](https://mlsanity.com/paper/2506.22900/) (2025)
- [A Reasoning-Enabled Vision-Language Foundation Model for Chest X-ray Interpretation](https://mlsanity.com/paper/2604.00493/) (2026)
- [VinDr-CXR-VQA: A Visual Question Answering Dataset for Explainable Chest X-Ray Analysis with Multi-Task Learning](https://mlsanity.com/paper/2511.00504/) (2025)
- [VT-Bench: A Unified Benchmark for Visual-Tabular Multi-Modal Learning](https://mlsanity.com/paper/2605.08146/) (2026)
- [AgentRx: A Benchmark Study of LLM Agents for Multimodal Clinical Prediction Tasks](https://mlsanity.com/paper/2605.10286/) (2026)
- [WoLF: Wide-scope Large Language Model Framework for CXR Understanding](https://mlsanity.com/paper/2403.15456/) (2024)
- [Towards Predicting Temporal Changes in a Patient's Chest X-ray Images based on Electronic Health Records](https://mlsanity.com/paper/2409.07012/) (2024)
- [Enhancing Multi-task Learning Capability of Medical Generalist Foundation Model via Image-centric Multi-annotation Data](https://mlsanity.com/paper/2504.09967/) (2025)
- [Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification](https://mlsanity.com/paper/2508.12418/) (2025)
- [Probing the Reliability of Driving VLMs: From Inconsistent Responses to Grounded Temporal Reasoning](https://mlsanity.com/paper/2603.09512/) (2026)
- [CodeClinic: Evaluating Automation of Coding Skills for Clinical Reasoning Agents](https://mlsanity.com/paper/2605.09675/) (2026)

## Abstract (arXiv)

Electronic Health Records (EHRs), which contain patients' medical histories in various multi-modal formats, often overlook the potential for joint reasoning across imaging and table modalities underexplored in current EHR Question Answering (QA) systems. In this paper, we introduce EHRXQA, a novel multi-modal question answering dataset combining structured EHRs and chest X-ray images. To develop our dataset, we first construct two uni-modal resources: 1) The MIMIC-CXR-VQA dataset, our newly created medical visual question answering (VQA) benchmark, specifically designed to augment the imaging modality in EHR QA, and 2) EHRSQL (MIMIC-IV), a refashioned version of a previously established table-based EHR QA dataset. By integrating these two uni-modal resources, we successfully construct a multi-modal EHR QA dataset that necessitates both uni-modal and cross-modal reasoning. To address the unique challenges of multi-modal questions within EHRs, we propose a NeuralSQL-based strategy equipped with an external VQA API. This pioneering endeavor enhances engagement with multi-modal EHR sources and we believe that our dataset can catalyze advances in real-world medical scenarios such as clinical decision-making and research. EHRXQA is available at https://github.com/baeseongsu/ehrxqa.