Med-Gemini sets new records on 10 of 14 medical benchmarks including 91.1% on MedQA-USMLE, beats GPT-4V by 44.5% on multimodal tasks, and surpasses humans on medical text summarization.
Towards accu- rate differential diagnosis with large language models,
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
The paper introduces an agentic AI platform to train and support recovered soldiers as peer facilitators providing mental health triage and interventions in austere battlefield environments.
Fine-tuning and data augmentation improve LLM performance on medical jargon extraction and prioritization from EHR notes, with augmented open-source models sometimes outperforming closed-source ones on 106 annotated notes.
citing papers explorer
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Capabilities of Gemini Models in Medicine
Med-Gemini sets new records on 10 of 14 medical benchmarks including 91.1% on MedQA-USMLE, beats GPT-4V by 44.5% on multimodal tasks, and surpasses humans on medical text summarization.
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Train the Trainers -- An Agentic AI Framework for Peer-Based Mental Health Support in Battlefield Environments
The paper introduces an agentic AI platform to train and support recovered soldiers as peer facilitators providing mental health triage and interventions in austere battlefield environments.
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Enhancing LLMs for Identifying and Prioritizing Important Medical Jargons from Electronic Health Record Notes Utilizing Data Augmentation
Fine-tuning and data augmentation improve LLM performance on medical jargon extraction and prioritization from EHR notes, with augmented open-source models sometimes outperforming closed-source ones on 106 annotated notes.