AfriMed-QA: A Pan-African, Multi-Specialty, Medical Question-Answering Benchmark Dataset
Charles Nimo, Tobi Olatunji, Abraham Toluwase Owodunni, Tassallah Abdullahi, Emmanuel Ayodele, Mardhiyah Sanni, Ezinwanne C. Aka, Folafunmi Omofoye
Abstract
Recent advancements in large language model (LLM) performance on medical multiplechoice question (MCQ) benchmarks have stimulated interest from healthcare providers and patients globally. Particularly in low-andmiddle-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 largescale 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.
BibTeX
@inproceedings{nimo-etal-2025-afrimed,
title = "{A}fri{M}ed-{QA}: A Pan-{A}frican, Multi-Specialty, Medical Question-Answering Benchmark Dataset",
author = {Nimo, Charles and
Olatunji, Tobi and
Owodunni, Abraham Toluwase and
Abdullahi, Tassallah and
Ayodele, Emmanuel and
Sanni, Mardhiyah and
Aka, Ezinwanne C. and
Omofoye, Folafunmi and
Yuehgoh, Foutse and
Faniran, Timothy and
Dossou, Bonaventure F. P. and
Yekini, Moshood O. and
Kemp, Jonas and
Heller, Katherine A and
Omeke, Jude Chidubem and
Md, Chidi Asuzu and
Etori, Naome A and
Ndiaye, A{\"i}m{\'e}rou and
Okoh, Ifeoma and
Ocansey, Evans Doe and
Kinara, Wendy and
Best, Michael L. and
Essa, Irfan and
Moore, Stephen Edward and
Fourie, Chris and
Asiedu, Mercy Nyamewaa},
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.96/",
doi = "10.18653/v1/2025.acl-long.96",
pages = "1948--1973",
ISBN = "979-8-89176-251-0"
}