evidence-based

FaithMed is a framework that trains LLMs for faithful evidence-based medical reasoning by integrating clinician-designed rubrics with reinforcement learning using step-level process reward assignment, achieving significant improvements over baselines on multiple medical benchmarks.

This perspective paper develops a conceptual and methodological framework for evaluating evidence-licensed claims in AI-assisted research, emphasizing calibration as a mechanism for managing scientific assertion rights and distinguishing between different AI research routes.

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.