AfriMed-QA: A Pan-African, Multi-Specialty, Medical Question-Answering Benchmark Dataset
This addresses the problem of evaluating LLMs for healthcare in low-resource African settings, but it is incremental as it primarily provides a new dataset.
The paper tackles the lack of medical question-answering benchmarks for Africa by introducing AfriMed-QA, a dataset of 15,000 questions from 16 countries, and finds that LLMs show significant performance variation, with biomedical models underperforming general ones and smaller models failing to pass.
Recent advancements in large language model(LLM) performance on medical multiple choice question (MCQ) benchmarks have stimulated interest from healthcare providers and patients globally. Particularly in low-and middle-income countries (LMICs) facing acute physician shortages and lack of specialists, LLMs offer a potentially scalable pathway to enhance healthcare access and reduce costs. However, their effectiveness in the Global South, especially across the African continent, remains to be established. In this work, we introduce AfriMed-QA, the first large scale Pan-African English multi-specialty medical Question-Answering (QA) dataset, 15,000 questions (open and closed-ended) sourced from over 60 medical schools across 16 countries, covering 32 medical specialties. We further evaluate 30 LLMs across multiple axes including correctness and demographic bias. Our findings show significant performance variation across specialties and geographies, MCQ performance clearly lags USMLE (MedQA). We find that biomedical LLMs underperform general models and smaller edge-friendly LLMs struggle to achieve a passing score. Interestingly, human evaluations show a consistent consumer preference for LLM answers and explanations when compared with clinician answers.