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Found 5 papers, 0 papers with code
A Toolbox for Surfacing Health Equity Harms and Biases in Large Language Models
Large language models (LLMs) hold immense promise to serve complex health information needs but also have the potential to introduce harm and exacerbate health disparities.
The Case for Globalizing Fairness: A Mixed Methods Study on Colonialism, AI, and Health in Africa
Whereas experts generally expressed a shared view about the relevance of colonial history for the development and implementation of AI technologies in Africa, the majority of the general population participants surveyed did not think there was a direct link between AI and colonialism.
Impact of Large Language Model Assistance on Patients Reading Clinical Notes: A Mixed-Methods Study
Augmentations were evaluated for errors by clinicians, and we found misleading errors occur, with errors more common in real donated notes than synthetic notes, illustrating the importance of carefully written clinical notes.
Network-Agnostic Knowledge Transfer for Medical Image Segmentation
The salient features of our algorithm include: 1)no need for original training data or generative networks, 2) knowledge transfer between different architectures, 3) ease of implementation for downstream tasks by using the downstream task dataset as the transferal dataset, 4) knowledge transfer of an ensemble of models, trained independently, into one student model.
Network-Agnostic Knowledge Transfer from Latent Dataset for Medical Image Segmentation
Transfer learning often employs all or part of the weights of a pre-trained net-work to the problem at hand; this limits the flexibility of new neural architectures.
