6 papers
MedGemma Technical Report
Andrew Sellergren, Sahar Kazemzadeh, Tiam Jaroensri +78
Artificial intelligence (AI) has significant potential in healthcare applications, but its training and deployment faces challenges due to healthcare's diverse data, complex tasks,…
AfriMed-QA: A Pan-African, Multi-Specialty, Medical Question-Answering Benchmark Dataset
Tobi Olatunji, Charles Nimo, Abraham Owodunni +23
Recent advancements in large language model(LLM) performance on medical multiple choice question (MCQ) benchmarks have stimulated interest from healthcare providers and patients gl…
Nteasee: Understanding Needs in AI for Health in Africa -- A Mixed-Methods Study of Expert and General Population Perspectives
Mercy Nyamewaa Asiedu, Iskandar Haykel, Awa Dieng +7
Artificial Intelligence (AI) for health has the potential to significantly change and improve healthcare. However in most African countries, identifying culturally and contextually…
Contextual Evaluation of Large Language Models for Classifying Tropical and Infectious Diseases
Mercy Asiedu, Nenad Tomasev, Chintan Ghate +9
While large language models (LLMs) have shown promise for medical question answering, there is limited work focused on tropical and infectious disease-specific exploration. We buil…
Impact of Large Language Model Assistance on Patients Reading Clinical Notes: A Mixed-Methods Study
Niklas Mannhardt, Elizabeth Bondi-Kelly, Barbara Lam +10
Large language models (LLMs) have immense potential to make information more accessible, particularly in medicine, where complex medical jargon can hinder patient comprehension of…
A Toolbox for Surfacing Health Equity Harms and Biases in Large Language Models
Stephen R. Pfohl, Heather Cole-Lewis, Rory Sayres +27
Large language models (LLMs) hold promise to serve complex health information needs but also have the potential to introduce harm and exacerbate health disparities. Reliably evalua…