99 citations · 199 across the 10 of their papers we have counts for
10 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…
The Case for Globalizing Fairness: A Mixed Methods Study on Colonialism, AI, and Health in Africa
Mercy Asiedu, Awa Dieng, Iskandar Haykel +7
With growing application of machine learning (ML) technologies in healthcare, there have been calls for developing techniques to understand and mitigate biases these systems may ex…
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…
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…
Machine Learning for Health symposium 2023 -- Findings track
Stefan Hegselmann, Antonio Parziale, Divya Shanmugam +5
A collection of the accepted Findings papers that were presented at the 3rd Machine Learning for Health symposium (ML4H 2023), which was held on December 10, 2023, in New Orleans,…
Globalizing Fairness Attributes in Machine Learning: A Case Study on Health in Africa
Mercy Nyamewaa Asiedu, Awa Dieng, Abigail Oppong +3
With growing machine learning (ML) applications in healthcare, there have been calls for fairness in ML to understand and mitigate ethical concerns these systems may pose. Fairness…
Network-Agnostic Knowledge Transfer for Medical Image Segmentation
Shuhang Wang, Vivek Kumar Singh, Alex Benjamin +5
Conventional transfer learning leverages weights of pre-trained networks, but mandates the need for similar neural architectures. Alternatively, knowledge distillation can transfer…