The work introduces uncertainty-aware foundation models for clinical data by learning set-valued patient representations that enforce consistency across partial observations and integrate multimodal self-supervised objectives.
Clinical text summarization: Adapting large language models can outperform human experts.Research Square
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Domain-specific models like ChatDoctor excel at medically accurate and contextually reliable text while general-purpose models like Grok and LLaMA perform better on structured medical question-answering tasks.
citing papers explorer
-
Uncertainty-Aware Foundation Models for Clinical Data
The work introduces uncertainty-aware foundation models for clinical data by learning set-valued patient representations that enforce consistency across partial observations and integrate multimodal self-supervised objectives.
-
Comparative Analysis of Large Language Models in Healthcare
Domain-specific models like ChatDoctor excel at medically accurate and contextually reliable text while general-purpose models like Grok and LLaMA perform better on structured medical question-answering tasks.