ArXiv research paper on foundation models designed for clinical data applications with integrated uncertainty quantification. Addresses a key challenge in deploying large language models in high-stakes medical domains where calibrated confidence estimates are critical.
Research
Uncertainty-Aware Foundation Models for Clinical Data
Researchers develop foundation models with integrated uncertainty quantification for clinical data, enabling LLMs to signal low confidence rather than hallucinate in high-stakes medical settings.
Tuesday, April 7, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.LG (Machine Learning)BY sys://pipeline
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