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Yuki Sahashi
@Yuki_Sahashi
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Cardiologist from Gifu, Japan Cedars-Sinai Medical Center (LA) Clinical AI and Cardiology / Echo_AI / Arrhthmia /father. Interest: ⚽️🀄️👨👨👧👦⚾️🧳🏥☕🎨🍖
Earth
Joined July 2019
RT @OpenAI: Today we're sharing a major update to the Model Spec—a document which defines how we want our models to behave. The update rei…
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RT @pnatarajanmd: Clinical Consensus Statement from @escardio on polygenic risk scoring for cardiovascular disease
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RT @EricTopol: How to get 146% return on investment? The NIH. That's just the economic benefit. Biomedical research promotes human healt…
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RT @EricTopol: Getting your real-time heart rhythm monitored for 10 days now requires an ECG technician to review and notify your doctor of…
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@kuroda_shunsuke @nebetake 事情あり、今月だけ課金しましたが、本当に深堀りしたいことなければまじでいらないですね・・・ なんというか調査コンサルティング雇う感じですね
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もちろん少しのズレなどはあるものの、すごい技術だなと思う。医療AI開発者側の視点から見て感服する。
Agentic AI Meets Medicine!!! 🔬 Excited to announce MedRAX: a groundbreaking Medical Reasoning Agent for Chest X-ray interpretation, now on arXiv! Paper: Code: What is MedRAX? MedRAX is the first versatile AI agent that seamlessly integrates state-of-the-art chest X-ray analysis tools and multimodal large language models into a unified framework, enabling dynamic reasoning for complex medical queries without additional training. 🎯 Why MedRAX? While specialized AI models excel at specific chest X-ray tasks, they often operate in isolation. Medical professionals need a unified, reliable system that can handle complex queries while maintaining accuracy. MedRAX bridges this gap! 🛠️ Integrated Tools: - Visual QA: CheXagent & LLaVA-Med - Segmentation: MedSAM & ChestX-Det - Report Generation: CheXpert Plus - Classification: TorchXRayVision - Grounding: Maira-2 - Synthetic Data: RoentGen 💡 Key Features: - Unified Framework: Seamlessly integrates specialized medical tools with multimodal large language model reasoning. - Dynamic Orchestration: Intelligent tool selection and coordination for complex queries. - Clinical Focus: Designed for real-world medical workflows and deployment. 📊 Introducing ChestAgentBench: We're also releasing ChestAgentBench, a comprehensive medical agent benchmark built from 675 expert-curated clinical cases, featuring 2,500 complex medical queries across 7 categories. Check it out: 🎉 Results speak for themselves: - 63.1% accuracy on ChestAgentBench - State-of-the-art performance on CheXbench - Outperforms both general-purpose and specialized medical models 🙏 Huge shoutout to @adibvafa, Jun Ma (@JunMa_11), AlifMunim, and Hongwei Lyu for their exceptional work on this project! @VectorInst
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@UofTCompSci
@UofT_LMP
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