WorldMedQA-V: a multilingual, multimodal medical examination dataset for multimodal language models evaluation

• URL: http://arxiv.org/abs/2410.12722v1
• Date: Wed, 16 Oct 2024 16:31:24 GMT
• Title: WorldMedQA-V: a multilingual, multimodal medical examination dataset for multimodal language models evaluation
• Authors: João Matos, Shan Chen, Siena Placino, Yingya Li, Juan Carlos Climent Pardo, Daphna Idan, Takeshi Tohyama, David Restrepo, Luis F. Nakayama, Jose M. M. Pascual-Leone, Guergana Savova, Hugo Aerts, Leo A. Celi, A. Ian Wong, Danielle S. Bitterman, Jack Gallifant,
• Abstract summary: Multimodal/vision language models (VLMs) are increasingly being deployed in healthcare settings worldwide. Existing datasets are largely text-only and available in a limited subset of languages and countries. WorldMedQA-V includes 568 labeled multiple-choice QAs paired with 568 medical images from four countries.
• Score: 4.149844666297669
• License:
• Abstract: Multimodal/vision language models (VLMs) are increasingly being deployed in healthcare settings worldwide, necessitating robust benchmarks to ensure their safety, efficacy, and fairness. Multiple-choice question and answer (QA) datasets derived from national medical examinations have long served as valuable evaluation tools, but existing datasets are largely text-only and available in a limited subset of languages and countries. To address these challenges, we present WorldMedQA-V, an updated multilingual, multimodal benchmarking dataset designed to evaluate VLMs in healthcare. WorldMedQA-V includes 568 labeled multiple-choice QAs paired with 568 medical images from four countries (Brazil, Israel, Japan, and Spain), covering original languages and validated English translations by native clinicians, respectively. Baseline performance for common open- and closed-source models are provided in the local language and English translations, and with and without images provided to the model. The WorldMedQA-V benchmark aims to better match AI systems to the diverse healthcare environments in which they are deployed, fostering more equitable, effective, and representative applications.

Related papers

• EMMA-500: Enhancing Massively Multilingual Adaptation of Large Language Models [50.459861376459656]
  EMMA-500 is a large-scale multilingual language model continue-trained on texts across 546 languages. Our results highlight the effectiveness of continual pre-training in expanding large language models' language capacity.
  arXiv  Detail & Related papers  (2024-09-26T14:40:45Z)
• CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark [68.21939124278065]
  Culturally-diverse multilingual Visual Question Answering benchmark designed to cover a rich set of languages and cultures. CVQA includes culturally-driven images and questions from across 30 countries on four continents, covering 31 languages with 13 scripts, providing a total of 10k questions. We benchmark several Multimodal Large Language Models (MLLMs) on CVQA, and show that the dataset is challenging for the current state-of-the-art models.
  arXiv  Detail & Related papers  (2024-06-10T01:59:00Z)
• Medical mT5: An Open-Source Multilingual Text-to-Text LLM for The Medical Domain [19.58987478434808]
  We present Medical mT5, the first open-source text-to-text multilingual model for the medical domain. A comprehensive evaluation shows that Medical mT5 outperforms both encoders and similarly sized text-to-text models for the Spanish, French, and Italian benchmarks.
  arXiv  Detail & Related papers  (2024-04-11T10:01:32Z)
• Towards Building Multilingual Language Model for Medicine [54.1382395897071]
  We construct a multilingual medical corpus, containing approximately 25.5B tokens encompassing 6 main languages. We propose a multilingual medical multi-choice question-answering benchmark with rationale, termed as MMedBench. Our final model, MMed-Llama 3, with only 8B parameters, achieves superior performance compared to all other open-source models on both MMedBench and English benchmarks.
  arXiv  Detail & Related papers  (2024-02-21T17:47:20Z)
• BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains [8.448541067852]
  Large Language Models (LLMs) have demonstrated remarkable versatility in recent years. Despite the availability of various open-source LLMs tailored for health contexts, adapting general-purpose LLMs to the medical domain presents significant challenges. We introduce BioMistral, an open-source LLM tailored for the biomedical domain, utilizing Mistral as its foundation model.
  arXiv  Detail & Related papers  (2024-02-15T23:39:04Z)
• MultiMedEval: A Benchmark and a Toolkit for Evaluating Medical Vision-Language Models [1.3535643703577176]
  MultiMedEval is an open-source toolkit for fair and reproducible evaluation of large, medical vision-language models (VLM) It comprehensively assesses the models' performance on a broad array of six multi-modal tasks, conducted over 23 datasets, and spanning over 11 medical domains. We open-source a Python toolkit with a simple interface and setup process, enabling the evaluation of any VLM in just a few lines of code.
  arXiv  Detail & Related papers  (2024-02-14T15:49:08Z)
• MedEval: A Multi-Level, Multi-Task, and Multi-Domain Medical Benchmark for Language Model Evaluation [22.986061896641083]
  MedEval is a multi-level, multi-task, and multi-domain medical benchmark to facilitate the development of language models for healthcare. With 22,779 collected sentences and 21,228 reports, we provide expert annotations at multiple levels, offering a granular potential usage of the data.
  arXiv  Detail & Related papers  (2023-10-21T18:59:41Z)
• MULTI3NLU++: A Multilingual, Multi-Intent, Multi-Domain Dataset for Natural Language Understanding in Task-Oriented Dialogue [115.32009638844059]
  We extend the English only NLU++ dataset to include manual translations into a range of high, medium, and low resource languages. Because of its multi-intent property, MULTI3NLU++ represents complex and natural user goals. We use MULTI3NLU++ to benchmark state-of-the-art multilingual models for the Natural Language Understanding tasks of intent detection and slot labelling.
  arXiv  Detail & Related papers  (2022-12-20T17:34:25Z)
• xGQA: Cross-Lingual Visual Question Answering [100.35229218735938]
  xGQA is a new multilingual evaluation benchmark for the visual question answering task. We extend the established English GQA dataset to 7 typologically diverse languages. We propose new adapter-based approaches to adapt multimodal transformer-based models to become multilingual.
  arXiv  Detail & Related papers  (2021-09-13T15:58:21Z)
• XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization [128.37244072182506]
  Cross-lingual TRansfer Evaluation of Multilinguals XTREME is a benchmark for evaluating the cross-lingual generalization capabilities of multilingual representations across 40 languages and 9 tasks. We demonstrate that while models tested on English reach human performance on many tasks, there is still a sizable gap in the performance of cross-lingually transferred models.
  arXiv  Detail & Related papers  (2020-03-24T19:09:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.