Clinical text classification under the Open and Closed Topic Assumptions
PulseAugur coverage of Clinical text classification under the Open and Closed Topic Assumptions — every cluster mentioning Clinical text classification under the Open and Closed Topic Assumptions across labs, papers, and developer communities, ranked by signal.
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AI clinical prediction research tackles uncertainty and correctness evaluation
Two new research papers explore the critical issue of uncertainty estimation in AI models used for clinical applications. The first paper introduces a novel Bayesian approach for large language models (LLMs) in clinical…
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New benchmark reveals LLMs struggle with diagnostic uncertainty in clinical text
A new benchmark has been developed to evaluate how well large language models (LLMs) preserve diagnostic uncertainty in clinical text. Researchers found that current LLMs often fail to maintain the original level of unc…
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Meddies PII: Open model for clinical text de-identification released
Researchers have introduced Meddies PII, an open-source model and dataset designed for de-identifying clinical text. The model aims to remove patient-specific information while preserving crucial clinical details necess…