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PathPocket is a multimodal AI agentic co-pilot for evidence-grounded pathology, utilizing a comprehensive evidence corpus and hypergraph to outperform existing state-of-the-art methods on over 200,000 real-world cases.
Proposes EVIDENT, a framework that integrates Bayesian training and evidence-based ranking for neural architecture selection, demonstrated on subject-specific blood glucose forecasting in type 1 diabetes, systematically selecting low-capacity models that generalize reliably.
This paper proposes an evidence-based model to automatically generate query keywords from query-free summarization datasets, enabling the creation of query-focused summarization datasets. Experimental results show that summaries generated using evidence-based queries achieve competitive ROUGE scores compared to original queries.
DeepER-Med introduces an agentic AI framework for evidence-based medical research with explicit evidence appraisal criteria and a new benchmark dataset (DeepER-MedQA) of 100 expert-curated medical questions, demonstrating superior performance over production platforms with clinical validation on real-world cases.