CHI-Bench shows current AI agents achieve at most 28% success on long-horizon healthcare workflows that require dense policy adherence, multi-role handoffs, and multi-turn interactions.
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3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
FastOMOP is a multi-agent architecture using process-boundary deterministic validation to deliver safe, auditable real-world evidence generation from OMOP CDM data across synthetic and real datasets.
HypEHR is a hyperbolic embedding model for EHR data that uses Lorentzian geometry and hierarchy-aware pretraining to answer clinical questions nearly as well as large language models but with much smaller size.
citing papers explorer
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CHI-Bench: Can AI Agents Automate End-to-End, Long-Horizon, Policy-Rich Healthcare Workflows?
CHI-Bench shows current AI agents achieve at most 28% success on long-horizon healthcare workflows that require dense policy adherence, multi-role handoffs, and multi-turn interactions.
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FastOMOP: A Foundational Architecture for Reliable Agentic Real-World Evidence Generation on OMOP CDM data
FastOMOP is a multi-agent architecture using process-boundary deterministic validation to deliver safe, auditable real-world evidence generation from OMOP CDM data across synthetic and real datasets.
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HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering
HypEHR is a hyperbolic embedding model for EHR data that uses Lorentzian geometry and hierarchy-aware pretraining to answer clinical questions nearly as well as large language models but with much smaller size.