Traj-CoA is a multi-agent LLM framework that sequentially processes noisy five-year EHR data via worker agents into EHRMem for manager-agent lung cancer risk prediction and outperforms four categories of baselines in zero-shot evaluation.
Large language models as biomedical hypothesis generators: A comprehensive evaluation
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ARIEL evaluates LLMs and LMMs on full-length biomedical summarization and figure interpretation with blinded expert review, identifies limitations, and demonstrates gains from prompt engineering, fine-tuning, and an integrated agent for hypothesis generation.
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Traj-CoA: Patient Trajectory Modeling via Chain-of-Agents for Lung Cancer Risk Prediction
Traj-CoA is a multi-agent LLM framework that sequentially processes noisy five-year EHR data via worker agents into EHRMem for manager-agent lung cancer risk prediction and outperforms four categories of baselines in zero-shot evaluation.
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Advancing AI Research Assistants with Expert-Involved Learning
ARIEL evaluates LLMs and LMMs on full-length biomedical summarization and figure interpretation with blinded expert review, identifies limitations, and demonstrates gains from prompt engineering, fine-tuning, and an integrated agent for hypothesis generation.