Papers by Michael L. Best
AfriMed-QA: A Pan-African, Multi-Specialty, Medical Question-Answering Benchmark Dataset (2025.acl-long)
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Charles Nimo, Tobi Olatunji, Abraham Toluwase Owodunni, Tassallah Abdullahi, Emmanuel Ayodele, Mardhiyah Sanni, Ezinwanne C. Aka, Folafunmi Omofoye, Foutse Yuehgoh, Timothy Faniran, Bonaventure F. P. Dossou, Moshood O. Yekini, Jonas Kemp, Katherine A Heller, Jude Chidubem Omeke, Chidi Asuzu Md, Naome A Etori, Aïmérou Ndiaye, Ifeoma Okoh, Evans Doe Ocansey, Wendy Kinara, Michael L. Best, Irfan Essa, Stephen Edward Moore, Chris Fourie, Mercy Nyamewaa Asiedu
| Challenge: | Recent advances in large language models (LLMs) performance on medical multiplechoice question (MCQ) benchmarks have stimulated interest from healthcare providers and patients globally. |
| Approach: | They introduce AfriMed-QA, the first largescale Pan-African English multi-specialty medical Question-Answering (QA) dataset, with 15,000 questions sourced from over 60 medical schools across 16 countries. |
| Outcome: | The proposed model outperforms other models in the medical field and is compared with other models. |
Africa Health Check: Probing Cultural Bias in Medical LLMs (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) are increasingly deployed in global healthcare . yet their outputs reflect Western-centric training data and omit indigenous medical systems . |
| Approach: | They evaluate cultural bias in instruction-tuned medical LLMs using a curated dataset of African traditional herbal medicine. |
| Outcome: | The findings show that cultural biases remain embedded in model training . the findings highlight the need for culturally informed evaluation strategies . |