ABRA shows radiology agents excel at tool execution (89%+) but struggle with outcomes (0-25%), with oracle perception raising outcomes to 69-100%, identifying perception as the primary bottleneck.
NEJM AI , volume =
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AgentCARD benchmark shows heterogeneous LLM agent teams with mixed deployments reach the cost-accuracy frontier, delivering up to 44% higher accuracy or 12x lower cost than uniform teams, with domain-specific role bottlenecks.
Intent-aware retrieval over assertion-labeled knowledge graphs improves clinical QA accuracy by 22 percentage points on a new MIMIC-IV benchmark that stresses negation, temporality, and attribution.
Presents PopMedQA benchmark and shows domain-independent LLM methods fail on token-inefficient longitudinal medical records, leaving room for domain-specific approaches.
A pre-response classifier predicts user rejection risk for clinical LLM outputs with AUROC 0.719 over 4.5 months of deployment data by incorporating deployment-specific context.
citing papers explorer
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ABRA: Agent Benchmark for Radiology Applications
ABRA shows radiology agents excel at tool execution (89%+) but struggle with outcomes (0-25%), with oracle perception raising outcomes to 69-100%, identifying perception as the primary bottleneck.
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Specialize Roles, Mix Deployments: Pushing the Cost-Accuracy Frontier of LLM Agent Teams
AgentCARD benchmark shows heterogeneous LLM agent teams with mixed deployments reach the cost-accuracy frontier, delivering up to 44% higher accuracy or 12x lower cost than uniform teams, with domain-specific role bottlenecks.
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ClinicalBench: Stress-Testing Assertion-Aware Retrieval for Cross-Admission Clinical QA on MIMIC-IV
Intent-aware retrieval over assertion-labeled knowledge graphs improves clinical QA accuracy by 22 percentage points on a new MIMIC-IV benchmark that stresses negation, temporality, and attribution.
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The Verbose Context Problem in Medical Records
Presents PopMedQA benchmark and shows domain-independent LLM methods fail on token-inefficient longitudinal medical records, leaving room for domain-specific approaches.
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Deployment-Centered Evaluation: Predicting Query-Level Rejection Risk in a Clinical LLM System
A pre-response classifier predicts user rejection risk for clinical LLM outputs with AUROC 0.719 over 4.5 months of deployment data by incorporating deployment-specific context.