Contrastive visual language pretraining has emerged as a powerful method for either
training new language-aware image encoders or augmenting existing pretrained models …

Advances in machine learning for health care have brought concerns about bias from the
research community; specifically, the introduction, perpetuation, or exacerbation of care …

Purpose To analyze a recently published chest radiography foundation model for the
presence of biases that could lead to subgroup performance disparities across biologic sex …

Despite recent advancements in machine learning (ML) applications in health care, there
have been few benefits and improvements to clinical medicine in the hospital setting. To …

In this work, we present an approach, which we call Embeddings for Language/Image-
aligned X-Rays, or ELIXR, that leverages a language-aligned image encoder combined or …

We present a radiology-specific multimodal model for the task for generating radiological
reports from chest X-rays (CXRs). Our work builds on the idea that large language model (s) …

Background Artificial intelligence (AI) systems for automated chest x-ray interpretation hold
promise for standardising reporting and reducing delays in health systems with shortages of …

Segmenting objects like cells or nuclei in biomedical microscopy data is a standard task
required for many downstream analyses. However, existing pre-trained models are …

We study cultural and socioeconomic diversity in contrastive vision-language models
(VLMs). Using a broad range of benchmark datasets and evaluation metrics, we bring to …

Background AI models have shown promise in performing many medical imaging tasks.
However, our ability to explain what signals these models have learned is severely lacking …