WorldMedQA-V: a multilingual, multimodal medical examination dataset for multimodal language models evaluation
This dataset addresses the need for more equitable and effective evaluation of AI systems in diverse healthcare settings, though it is incremental as it builds on existing medical QA datasets by adding multimodal and multilingual features.
The authors tackled the lack of robust multilingual and multimodal benchmarks for evaluating vision language models in healthcare by introducing WorldMedQA-V, a dataset with 568 labeled multiple-choice questions paired with medical images from four countries, providing baseline performance for various models.
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.