Introduces Wasserstein equilibrium decoding that improves accuracy and convergence speed for small VLMs on medical VQA benchmarks by using semantic consensus instead of lexical order.
arXiv preprint arXiv:2504.17119 (2025)
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TF-IDF with LGBM achieved the highest AUC-ROC of 0.80 and best balance in predicting next-day discharge from clinical notes, outperforming fine-tuned compact LLMs like DistilGPT-2.
ECG foundation models for signal interpretation and medical LLMs for reasoning can be integrated into agentic systems for real-time cardiovascular intelligence on edge devices.
citing papers explorer
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Wasserstein Equilibrium Decoding for Reliable Medical Visual Question Answering
Introduces Wasserstein equilibrium decoding that improves accuracy and convergence speed for small VLMs on medical VQA benchmarks by using semantic consensus instead of lexical order.
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Resource-Conscious Modeling for Next- Day Discharge Prediction Using Clinical Notes
TF-IDF with LGBM achieved the highest AUC-ROC of 0.80 and best balance in predicting next-day discharge from clinical notes, outperforming fine-tuned compact LLMs like DistilGPT-2.
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ECG Foundation Models and Medical LLMs for Agentic Cardiovascular Intelligence at the Edge: A Review and Outlook
ECG foundation models for signal interpretation and medical LLMs for reasoning can be integrated into agentic systems for real-time cardiovascular intelligence on edge devices.