Single-agent LLM frameworks outperform naive multi-agent systems in multimodal clinical risk prediction tasks and are better calibrated.
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5 Pith papers cite this work, alongside 32 external citations. Polarity classification is still indexing.
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Multimodal LLMs performed similarly across models and better than standard care on diagnostic accuracy and patient safety in a real-world LMIC hospital dataset.
CARIS is a new agentic LLM framework that automates clinical research workflows from planning to reporting in a coding-free and privacy-preserving manner, achieving high completeness scores on heterogeneous datasets.
Proposes a multi-layer framework and agent architecture that operationalizes adaptation, coherence, continuity, and agency for longitudinal health AI agents.
RAG-based LLM extraction reaches 89% accuracy on clinical trial protocols versus 62.6% for standalone models and cuts simulated workflow time by 40%.
citing papers explorer
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AgentRx: A Benchmark Study of LLM Agents for Multimodal Clinical Prediction Tasks
Single-agent LLM frameworks outperform naive multi-agent systems in multimodal clinical risk prediction tasks and are better calibrated.
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Evaluating Multimodal LLMs for Inpatient Diagnosis: Real-World Performance, Safety, and Cost Across Ten Frontier Models
Multimodal LLMs performed similarly across models and better than standard care on diagnostic accuracy and patient safety in a real-world LMIC hospital dataset.
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Coding-Free and Privacy-Preserving Agentic Framework for Data-Driven Clinical Research
CARIS is a new agentic LLM framework that automates clinical research workflows from planning to reporting in a coding-free and privacy-preserving manner, achieving high completeness scores on heterogeneous datasets.
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A Framework for Longitudinal Health AI Agents
Proposes a multi-layer framework and agent architecture that operationalizes adaptation, coherence, continuity, and agency for longitudinal health AI agents.
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AI-assisted Protocol Information Extraction For Improved Accuracy and Efficiency in Clinical Trial Workflows
RAG-based LLM extraction reaches 89% accuracy on clinical trial protocols versus 62.6% for standalone models and cuts simulated workflow time by 40%.