The exceptionally rapid development of highly flexible, reusable artificial intelligence (AI)
models is likely to usher in newfound capabilities in medicine. We propose a new paradigm …

Advancements in deep learning and computer vision provide promising solutions for
medical image analysis, potentially improving healthcare and patient outcomes. However …

In tasks involving the interpretation of medical images, suitably trained machine-learning
models often exceed the performance of medical experts. Yet such a high-level of …

Existing vision-text contrastive learning like CLIP aims to match the paired image and
caption embeddings while pushing others apart, which improves representation …

The accelerated adoption of digital pathology and advances in deep learning have enabled
the development of robust models for various pathology tasks across a diverse array of …

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

Multi-modal data abounds in biomedicine, such as radiology images and reports.
Interpreting this data at scale is essential for improving clinical care and accelerating clinical …

Learning visual representations of medical images (eg, X-rays) is core to medical image
understanding but its progress has been held back by the scarcity of human annotations …

While multi-modal foundation models pre-trained on large-scale data have been successful
in natural language understanding and vision recognition, their use in medical domains is …

Contrastive pretraining on parallel image-text data has attained great success in vision-
language processing (VLP), as exemplified by CLIP and related methods. However, prior …