Public articles linked to the same research event.
medRxiv This work presents MES, a multi-agent framework of six specialized agents (Supervisor, LiteratureMiner, RWD-Analyst, KG-Specialist, Statistician, SafeChecker) that integrates published literature and trial evidence, generates question-specific real-world evidence from real-world clinical data, and queries biomedical knowledge graphs while preserving source traceability; evaluated in two clinical use cases (an Alzheimer disease medication question with no matching literature and a septic shock beta-blocker question with inconsistent randomized evidence) and across 144 clinical queries spanning six evidence-based medicine categories, it produced structured reports that preserved source traceability, identified cross-source disagreement and communicated uncertainty, and its SafeChecker improv
This work presents MES, a multi-agent framework of six specialized agents (Supervisor, LiteratureMiner, RWD-Analyst, KG-Specialist, Statistician, SafeChecker) that integrates published literature and trial evidence, generates question-specific real-world evidence from real-world clinical data, and queries biomedical knowledge graphs while preserving source traceability; evaluated in two clinical use cases (an Alzheimer disease medication question with no matching literature and a septic shock beta-blocker question with inconsistent randomized evidence) and across 144 clinical queries spanning six evidence-based medicine categories, it produced structured reports that preserved source traceability, identified cross-source disagreement and communicated uncertainty, and its SafeChecker improv
This work presents MES, a multi-agent framework of six specialized agents (Supervisor, LiteratureMiner, RWD-Analyst, KG-Specialist, Statistician, SafeChecker) that integrates published literature and trial evidence, generates question-specific real-world evidence from real-world clinical data, and queries biomedical knowledge graphs while preserving source traceability; evaluated in two clinical use cases (an Alzheimer disease medication question with no matching literature and a septic shock beta-blocker question with inconsistent randomized evidence) and across 144 clinical queries spanning six evidence-based medicine categories, it produced structured reports that preserved source traceability, identified cross-source disagreement and communicated uncertainty, and its SafeChecker improv
This work presents MES, a multi-agent framework of six specialized agents (Supervisor, LiteratureMiner, RWD-Analyst, KG-Specialist, Statistician, SafeChecker) that integrates published literature and trial evidence, generates question-specific real-world evidence from real-world clinical data, and queries biomedical knowledge graphs while preserving source traceability; evaluated in two clinical use cases (an Alzheimer disease medication question with no matching literature and a septic shock beta-blocker question with inconsistent randomized evidence) and across 144 clinical queries spanning six evidence-based medicine categories, it produced structured reports that preserved source traceability, identified cross-source disagreement and communicated uncertainty, and its SafeChecker improv