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Multimodal Question Answering in the Medical Domain: A summary of Existing Datasets and Systems
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| 2026-07-24 | 317 |
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| 2026-08-06 | 317 |
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# Existing Medical QA & VQA Datasets Multimodal Question Answering (QA) in the Medical Domain: A summary of Existing Datasets and Systems - I prepared this summary for my CMU/LTI talk on multimodal QA. My slides are available at https://www.slideshare.net/benabacha/multimodal-question-answering-in-the-medical-domain-cmulti-2020 - This list is not exhaustive. You can email me links and references of relevant medical QA datasets and systems and I'll update the list asap. Also, several challenge-related datasets are not publicly available anymore. You can contact the organizers to have the data. *** Two Main Tasks: Medical Question Answering (QA) & Visual Question Answering (VQA) *** I) Medical QA Datasets: ======================= 1) Corpus for Evidence Based Medicine Summarization (Mollá, 2010): https://sourceforge.net/projects/ebmsumcorpus 2) CLEF QA4MRE Alzheimer’s task (Peñas et al, 2012). 3) BioASK datasets (2012-2020): http://bioasq.org/participate/challenges 4) TREC LiveQA-Med (Ben Abacha et al, 2017): https://github.com/abachaa/LiveQA_MedicalTask_TREC2017 5) MEDIQA-2019 datasets on NLI, RQE, and QA (Ben Abacha et al., 2019): https://github.com/abachaa/MEDIQA2019 6) MEDIQA-AnS dataset of question-driven summaries of answers (Savery et al., 2020): https://osf.io/fyg46/ Paper: https://www.nature.com/articles/s41597-020-00667-z 7) MedQuaD Collection of 47k QA pairs (Ben Abacha and Demner-Fushman, 2019): https://github.com/abachaa/MedQuAD 8) Medication QA Collection (Ben Abacha et al., 2019): https://github.com/abachaa/Medication_QA_MedInfo2019 9) Consumer Health Question Summarization (Ben Abacha and Demner-Fushman, 2019): https://github.com/abachaa/MeQSum 10) emrQA: QA on Electronic Medical Records (Pampari et al., 2018). Scripts to generate emrQA from i2b2 data: https://github.com/panushri25/emrQA 11) EPIC-QA dataset on COVID-19 (Goodwin et al., 2020): https://bionlp.nlm.nih.gov/epic_qa/ 12) BiQA Corpus (Lamurias et al., 2020): https://github.com/lasigeBioTM/BiQA Paper:https://ieeexplore.ieee.org/document/9184044 13) HealthQA Dataset (Zhu et al., 2019): https://github.com/mingzhu0527/HAR Paper: https://dmkd.cs.vt.edu/papers/WWW19.pdf 14) MASH-QA Dataset on Multiple Answer Spans Healthcare Question Answering, with 35k QA pairs (Zhu et al., 2020): https://github.com/mingzhu0527/MASHQA Paper: https://www.aclweb.org/anthology/2020.findings-emnlp.342.pdf 15) MedMCQA: A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering. (Pal et al., CHIL, PMLR 2022): https://github.com/medmcqa/medmcqa Paper: https://proceedings.mlr.press/v174/pal22a.html II) Medical VQA Datasets (Radiology): ===================================== 1) VQA-RAD (Lau et al. 2018): https://osf.io/89kps 2) VQA-Med 2018 (Hasan et al. 2018): https://www.aicrowd.com/challenges/imageclef-2018-vqa-med 3) VQA-Med 2019 (Ben Abacha et al. 2019): https://github.com/abachaa/VQA-Med-2019 4) VQA-Med 2020 (Ben Abacha et al. 2020): https://github.com/abachaa/VQA-Med-2020 III) Online QA Systems: ======================== -- I searched and tested several systems (e.g. AskHERMES, MiPACQ, SimQ). This list includes only the systems that are still maintained. 1) CHiQA (Consumer Health Question Answering System): chiqa.nlm.nih.gov 2) Neural Covidex: covidex.ai IV) Medical Datasets Relevant to Question Answering: ===================================================== 1) i2b2 shared tasks (2006-2016): www.i2b2.org/NLP 2) n2c2 NLP clinical challenges (2018-2019): https://n2c2.dbmi.hms.harvard.edu https://dbmi.hms.harvard.edu/programs/national-nlp-clinical-challenges-n2c2 3) TREC Medical Records Track (2012-2013). 4) TREC Clinical Decision Support Track (2014-2016): http://www.trec-cds.org 5) TREC Precision Medicine Track (2017-2019): http://www.trec-cds.org 6) CLEF eHealth (2013-2020): https://clefehealth.imag.fr 7) COVID dataset (CORD-19): https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge V) Medical
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matched fp:d1114a798ee273a3, topic:question-answering