Asma Ben Abacha

MedQuAD. Medical Question Answering Dataset of 47,457 QA pairs created from 12 NIH websites

Existing-Medical-QA-Datasets. Multimodal Question Answering in the Medical Domain: A summary of Existing Datasets and Systems

MTS-Dialog. A new collection of 1.7k doctor-patient conversations and corresponding clinical notes/summaries.

VQA-Med-2019. Visual Question Answering in the Medical Domain VQA-Med 2019

MEDIQA-Chat-2023. MEDIQA-Chat Shared Tasks @ ACL-ClinicalNLP 2023

LiveQA_MedicalTask_TREC2017. Medical Question-Answering datasets prepared for the TREC 2017 LiveQA challenge (Medical Task)

MEDIQA2019. Challenge on Textual Inference and Question Entailment in the Medical Domain https://sites.google.com/view/mediqa2019

MEDEC.

MeQSum. Dataset for medical question summarization introduced in the ACL 2019 paper "On the Summarization of Consumer Health Questions" (A. Ben Abacha & D. Demner-Fushman)

MEDIQA2021. Python

VQA-Med-2021. VQA-Med 2021

Medication_QA_MedInfo2019. The gold standard corpus for medication question answering introduced in the MedInfo 2019 paper (Bridging the Gap between Consumers’ Medication Questions and Trusted Answers)

MEDIQA-CORR-2024. Jupyter Notebook

3D-MIR. 3D Medical Image Retrieval in Radiology

VQA-Med-2020. VQA-Med 2020

RQE_Data_AMIA2016. The medical question entailment data introduced in the AMIA 2016 Paper (Recognizing Question Entailment for Medical Question Answering)

EvaluationMetrics-ACL23. Python

ImageCLEF-CaptionTask-2021. ImageCLEFmed 2021 - Caption Prediction and Concept Detection Tasks

clinical_visit_note_summarization_corpus. A corpus of textual data corresponding to synthetic clinical encounters, including each encounters’ dialogue transcript and clinical notes.

MEDIQA-SYNUR-2026. Python