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Large language models (LLMs) are increasingly explored for clinical reasoning, but whether they appropriately revise judgments as patient evidence evolves remains unclear. We evaluated longitudinal belief updating using matched intensive-care trajectories from electronic health records. Across diverse LLMs, conditionin...
CHESTPHENOT is a compact 0.5-3B language model that jointly extracts finding labels, three-class status (present/absent/uncertain), and verbatim supporting evidence spans and demonstrates that locally deployable models can provide competitive and directly auditable radiology-report extraction without relying on externa...
Multimodal artificial intelligence (AI) agents are emerging in healthcare as systems that integrate heterogeneous clinical data, foundation models (FMs), tools, and agentic workflows, but their applications and translational readiness remain unclear. We conducted a scoping review of 37 peer-reviewed studies publish...
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