The increasing availability of biomedical data from large biobanks, electronic health records,
medical imaging, wearable and ambient biosensors, and the lower cost of genome and …

This article discusses the opportunities, applications and future directions of large-scale
pretrained models, ie, foundation models, which promise to significantly improve the …

Transformer, the model of choice for natural language processing, has drawn scant attention
from the medical imaging community. Given the ability to exploit long-term dependencies …

During the diagnostic process, clinicians leverage multimodal information, such as the chief
complaint, medical images and laboratory test results. Deep-learning models for aiding …

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 …

Dataset Condensation aims to condense a large dataset into a smaller one while
maintaining its ability to train a well-performing model, thus reducing the storage cost and …

Deep neural networks have been integrated into the whole clinical decision procedure
which can improve the efficiency of diagnosis and alleviate the heavy workload of …

Contrastive Language-Image Pre-training (CLIP), a simple yet effective pre-training
paradigm, successfully introduces text supervision to vision models. It has shown promising …

Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …

Artificial intelligence (AI) models for automatic generation of narrative radiology reports from
images have the potential to enhance efficiency and reduce the workload of radiologists …