Large language models (LLMs) have shown potential for medical and health question answering across various health-related tests spanning different formats and sources, such as multiple choice and short answer exam questions (e.g., USMLE MedQA), summarization, and clinical note taking, among others. Especially in low-resource settings, LLMs can potentially serve as valuable decision-support tools, enhancing clinical diagnostic accuracy and accessibility, and providing multilingual clinical decision support and health training, all of which are especially valuable at the community level.
Despite their success on existing medical benchmarks, there is uncertainty about whether these models generalize to tasks involving distribution shifts in disease types, contextual differences across symptoms, or variations in language and linguistics, even within English. Further, localized cultural contexts and region-specific medical knowledge is important for models deployed outside of traditional Western settings. Yet without diverse benchmark datasets that reflect the breadth of real-world contexts, it’s impossible to train or evaluate models in these settings, highlighting the need for more diverse benchmark datasets.
To address this gap, we present AfriMed-QA, a benchmark question–answer dataset that brings together consumer-style questions and medical school–type exams from 60 medical schools, across 16 countries in Africa. We developed the dataset in collaboration with numerous partners, including Intron health, Sisonkebiotik, University of Cape Coast, the Federation of African Medical Students Association, and BioRAMP, which collectively form the AfriMed-QA consortium, and with support from PATH/The Gates Foundation. We evaluated LLM responses on these datasets, comparing them to answers provided by human experts and rating their responses according to human preference. The methods used in this project can be scaled to other locales where digitized benchmarks may not currently be available.
