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Electronic Health Records (EHRs), which contain patients' medical historiesin various multi-modal formats, often overlook the potential for jointreasoning across imaging and table modalities underexplored in current EHRQuestion Answering (QA) systems. In this paper, we introduce EHRXQA, a novelmulti-modal question answering dataset combining structured EHRs and chestX-ray images. To develop our dataset, we first construct two uni-modalresources: 1) The MIMIC-CXR-VQA dataset, our newly created medical visualquestion answering (VQA) benchmark, specifically designed to augment theimaging modality in EHR QA, and 2) EHRSQL (MIMIC-IV), a refashioned version ofa previously established table-based EHR QA dataset. By integrating these twouni-modal resources, we successfully construct a multi-modal EHR QA datasetthat necessitates both uni-modal and cross-modal reasoning. To address theunique challenges of multi-modal questions within EHRs, we propose aNeuralSQL-based strategy equipped with an external VQA API. This pioneeringendeavor enhances engagement with multi-modal EHR sources and we believe thatour dataset can catalyze advances in real-world medical scenarios such asclinical decision-making and research. EHRXQA is available athttps://github.com/baeseongsu/ehrxqa.

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