AfriMed-QA: A Pan-African, Multi-Specialty, Medical Question-Answering Benchmark Dataset

• URL: http://arxiv.org/abs/2411.15640v3
• Date: Tue, 14 Jan 2025 05:35:08 GMT
• Title: AfriMed-QA: A Pan-African, Multi-Specialty, Medical Question-Answering Benchmark Dataset
• Authors: Tobi Olatunji, Charles Nimo, Abraham Owodunni, Tassallah Abdullahi, Emmanuel Ayodele, Mardhiyah Sanni, Chinemelu Aka, Folafunmi Omofoye, Foutse Yuehgoh, Timothy Faniran, Bonaventure F. P. Dossou, Moshood Yekini, Jonas Kemp, Katherine Heller, Jude Chidubem Omeke, Chidi Asuzu MD, Naome A. Etori, Aimérou Ndiaye, Ifeoma Okoh, Evans Doe Ocansey, Wendy Kinara, Michael Best, Irfan Essa, Stephen Edward Moore, Chris Fourie, Mercy Nyamewaa Asiedu,
• Abstract summary: We introduce AfriMed-QA, the first large scale Pan-African English multi-choice medical Question-Answering dataset.<n>15,000 questions were sourced from over 60 medical schools across 16 countries, covering 32 medical specialties.<n>We find that biomedical LLMs underperform general models and smaller edge-friendly LLMs struggle to achieve a passing score.
• Score: 8.521691388707799
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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