Vision-language foundation models (VLMs) for computed tomography (CT) are emerging tools that learn generalizable representations from large-scale clinical imaging data. While these models can predict task-specific labels, the extent to which their representations capture the clinical, physiological, and longitudinal...
C. Beeche, Joonghyun Kim, H. Tavolinejad et al.· npj Digital Medicine· 1 citation
The rapid adoption of large language models has enabled the development of clinical multi-agent systems (MAS) capable of integrating multimodal patient data and supporting increasingly complex clinical decision-making. However, the deployment of these systems in real-world healthcare settings raises critical ethical co...
Rakesh Sharma, S. Pugh, C. Beeche et al.· 0 citations
This study shows that integrating plasma proteomics with multi-organ imaging provides a comprehensive pan-organ imaging-proteomics map and reveals molecular pathways linking circulating proteins to human organ biology.
Zi-Rui Fan, J. Chirinos, Xiao-Chen Yang et al.· Nature Communications· 0 citations
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