How well do large language models understand health questions in African contexts?

Google Research’s new #AfriMed-QA benchmark takes a major step toward evaluating and improving AI’s performance in diverse global health settings.

#HealthcareAI #GlobalHealth #AIResearch #ResponsibleAI

And the timing couldn’t be better.
Projects like Google’s AfriMed-QA — a dataset advancing AI for African health, show why local context & culturally relevant AI matter in global healthcare.

🔗 https://research.google/blog/afrimed-qa-benchmarking-large-language-models-for-global-health/

AfriMed‑QA, with 15,000 questions from 60 medical schools in 16 countries, evaluated 30 LLMs and found gaps in relevance and bias. AI explanations beat clinicians. https://getnews.me/afrimed-qa-new-pan-african-benchmark-shows-llm-gaps-in-medical-qa/ #afrimmedqa #globalhealth

AfriMed-QA: Testing LLMs for Global Health

AfriMed-QA is a new benchmark assessing large language models' ability to answer questions about African medical literature, addressing knowledge gaps & promoting responsible global health AI.

Tobi Olatunji, Charles Nimo, Abraham Owodunni, Tassallah Abdullahi, Emmanuel Ayodele, Mardhiyah Sanni, Chinemelu Aka, Folafunmi Omofoye, Foutse Yuehgoh, Timothy Fani...
AfriMed-QA: A Pan-African, Multi-Specialty, Medical Question-Answering Benchmark Dataset
https://arxiv.org/abs/2411.15640

[2024-11-25] 📚 Updates in #LM&MA
🔍 More at researchtrend.ai/communities/LM&MA

(1) MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs
(2) AfriMed-QA: A Pan-African, Multi-Specialty, Medical Question-Answering Benchmark Dataset