Many clinical tasks require an understanding of specialized data, such as medical images
and genomics, which is not typically found in general-purpose large multimodal models …

Background Medicine is inherently multimodal, requiring the simultaneous interpretation
and integration of insights between many data modalities spanning text, imaging, genomics …

Driven by the large foundation models, the development of artificial intelligence has
witnessed tremendous progress lately, leading to a surge of general interest from the public …

In this study, we aim to initiate the development of Radiology Foundation Model, termed as
RadFM. We consider the construction of foundational models from the perspectives of data …

Recent advancements in artificial intelligence (AI) have precipitated significant
breakthroughs in healthcare, particularly in refining diagnostic procedures. However …

Despite the progress in automatic detection of radiologic findings from chest X-ray (CXR)
images in recent years, a quantitative evaluation of the explainability of these models is …

Current medical artificial intelligence systems are often limited to narrow applications,
hindering their widespread adoption in clinical practice. To address this limitation, we …

Background: In the evolving field of health care, multimodal generative artificial intelligence
(AI) systems, such as ChatGPT-4 with vision (ChatGPT-4V), represent a significant …

Excellence in a wide variety of medical applications poses considerable challenges for AI,
requiring advanced reasoning, access to up-to-date medical knowledge and understanding …

Whether it is adjusting parameters for MR pulse sequences, specifying parameters to
optimize radiation exposure on CT scanners, or programming complex treatment planning …