AMI reduces sensor usage by 48.8% and improves accuracy by 1.9% on average across three medical datasets by jointly learning when to sense and how to infer from multimodal physiological signals.
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CARE-ECG unifies ECG representation learning, causal graph-based diagnosis, and counterfactual assessment in an agentic LLM pipeline to improve accuracy and explanation faithfulness.
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Sense Less, Infer More: Agentic Multimodal Transformers for Edge Medical Intelligence
AMI reduces sensor usage by 48.8% and improves accuracy by 1.9% on average across three medical datasets by jointly learning when to sense and how to infer from multimodal physiological signals.
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CARE-ECG: Causal Agent-based Reasoning for Explainable and Counterfactual ECG Interpretation
CARE-ECG unifies ECG representation learning, causal graph-based diagnosis, and counterfactual assessment in an agentic LLM pipeline to improve accuracy and explanation faithfulness.