Zhihong Chen
@zhjohnchan
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Excited to introduce Cognita with @loublanks and @Dr_ASChaudhari. The field of radiology AI has been developing for years. My observations are (1) Products have emerged over time, but they haven’t unlocked compelling use cases; (2) The hype-driven environment incentivizes people
jobs.ashbyhq.com
Cognita Imaging Inc. Jobs
Today, @zhjohnchan, @Dr_ASChaudhari and I introduce Cognita. Over half the world lacks access to sufficient healthcare. We started Cognita a year ago to address this. Radiology is (1) the first-line diagnostic specialty, (2) facing a worsening workforce shortage, and (3) highly
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We are assembling a lean team of engineers and researchers. If you're interested in making large-scale clinical impact on healthcare with AI, we would love to hear from you! Let us know here:
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We are excited to announce the launch of our company - Cognita! We are working towards building the future of radiology through multi-modal AI systems with a great group of founders @loublanks, @Dr_ASChaudhari , and I, and advisors Ajit Singh, Chris Re, and @curtlanglotz.
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Very interesting paper! It would also be interesting to see if LLMs are good evaluators of novel ideas (e.g., by predicting the outstanding ICLR papers this year 😄).
Automating AI research is exciting! But can LLMs actually produce novel, expert-level research ideas? After a year-long study, we obtained the first statistically significant conclusion: LLM-generated ideas are more novel than ideas written by expert human researchers.
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🎉📝 Immensely honored to share that our #RoentGen model for creating synthetic chest X-rays is now published in @natBME! 📝 https://t.co/ErGXz87cye 1/🧵@PierreChambon6 @curtlanglotz @iScienceLuvr @Dr_ASChaudhari @StanfordAIMI
#AI #Radiology #GenAI
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🌟 Check out @askalphaxiv led by @rehaanahmad171!
arXiv -> alphaXiv Students at Stanford have built alphaXiv, an open discussion forum for arXiv papers. @askalphaxiv You can post questions and comments directly on top of any arXiv paper by changing arXiv to alphaXiv in any URL!
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How can we regulate Health #AI to maximize benefits while minimizing risks? @StanfordHAI's Associate Director @curtlanglotz shares his expert insights on creating a safer AI-driven healthcare environment for all. https://t.co/PSVRhOD6z0
hai.stanford.edu
HAI Associate Director Curt Langlotz explains the current state of health regulation and where we need to move to protect patients and better assist doctors.
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We're very excited to release 🌟DiVA — Distilled Voice Assistant 🔊 @WilliamBarrHeld ✅End-to-end differentiable speech LM; early fusion with Whisper and Llama 3 8B ✅Improves generalization by using distillation rather than supervised loss ✅Trained only using open-access
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Two weeks of learning, research, mentorship & fun wrapped up with the conclusion of the AIMI Sumer Research Internship & Bootcamp! Thanks to our participants, staff, mentors & featured speakers who made this year's programs a huge success!
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Excited to share our R-Tuning got an outstanding paper award@NAACL 2024! Take a look at this paper to see how to align your LLMs to honesty. https://t.co/DWZ1nTQalJ This work is finished during my visit at UIUC. Thanks for Prof. Ji and Prof. Zhang’s supervision!
arxiv.org
Large language models (LLMs) have revolutionized numerous domains with their impressive performance but still face their challenges. A predominant issue is the propensity for these models to...
We have won two NAACL2024 Outstanding Paper Awards! Congratulations to Chi Han, Shizhe Diao, Yi Fung, Xingyao Wang, Yangyi Chen and all students and collaborators! Chi Han @Glaciohound will be on academic job market next year! https://t.co/53MGP05Ek0
https://t.co/Z0ktxFqlrq
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Introduce HumanPlus - Shadowing part Humanoids are born for using human data. We build a real-time shadowing system using a single RGB camera and a whole-body policy for cloning human motion. Examples: - boxing🥊 - playing the piano🎹/ping pong - tossing - typing Open-sourced!
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⭐️ Check out Merlin led by @loublanks and mentor @Dr_ASChaudhari in this thread. Trained with diagnostic codes and rad reports and evaluated across various tasks comprehensively. @loublanks really laid a *solid foundation* for CT *foundation* models. :)
🧙 Excited to introduce Merlin, a vision language foundation model for 3D computed tomography 🐈⬛🩻 Trained to understand 3D abdominal CT scans using supervision from: 💾 Structured electronic health records (1.8+ million codes) 🗒️ Natural language radiology reports (6+ million
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⭐️ Explore CheXpert-Plus in this thread, a CXR dataset including radiology reports, demographics, and structured labels @StanfordAIMI 📄 Paper: https://t.co/T3hWa5lPdu 💾 Dataset: https://t.co/dTr5mDFDjh Led by @PierreChambon6, @IAMJBDEL, and @curtlanglotz.
Five years ago, thanks to the leadership of @mattlungrenMD, @stanfordAIMI released the CheXpert images: 223K JPG CXRs with labels for 14 conditions. CheXpert has been cited >6000 times, mostly related to development of supervised learning methods. Much has changed since then.🧵
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Our clinical #NLP work just published in @NatureMedicine! We present a framework to adapt & evaluate #LLMs for summarization. Physicians 🩺 prefer #LLM summaries to those of #medical experts❗ Big step to reduce documentation 📚 and focus more on personalized care 🙌 A 🧵
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⭐️ We are organizing two shared tasks at BioNLP @aclmeeting: 🩺 Radiology Report Generation and 📄 Discharge Me!, where we curated the ``Interpret-CXR'' dataset, containing over 725K CXR reports. 🩺 RRG-24: https://t.co/bQYo6WqmEn 📄 Discharge Me!:
stanford-aimi.github.io
Shared task on Large-Scale Radiology Report Generation @ BioNLP ACL’24
🧵[1/3]🚀Exciting news from AIMI! We're leading the charge in #ClinicalTextGeneration with two pivotal tasks at #BioNLP2024 & #ACL2024: 🩺 Radiology Report Generation, enhancing clinical communication via AI-generated X-ray reports, & 📄 Discharge Me!, streamlining EHR notes to
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Professor Yann LeCun (@ylecun) was profiled by @TIME earlier today on the importance of open-source AI systems. Read it here: https://t.co/tyHMO1xECc
time.com
Yann LeCun has long been a believer in the power of neural networks. He's also an advocate for an open source approach.
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Grateful to have had the opportunity to contribute a bit and support this project! Project was led by amazing researchers at @StanfordAIMI website: https://t.co/Zo4toKC8dJ arXiv: https://t.co/QM5ogpAQIY Check it out!
arxiv.org
Over 1.4 billion chest X-rays (CXRs) are performed annually due to their cost-effectiveness as an initial diagnostic test. This scale of radiological studies provides a significant opportunity to...
We're excited to share a recent paper by @StanfordAIMI that we had the opportunity to collaborate on: CheXagent: Towards a Foundation Model for Chest X-Ray Interpretation Website: https://t.co/LSyt4k2RdY We present and release a 8B multimodal radiology foundation model!
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Great teamwork with co-first authors @zhjohnchan @mayavarma23 @JB38076320 + tech/clinical collaborators @magdapasc, @louisblanke @dave_van_veen @loublanks @jeyamariajose @Alaa_Youssef92 @josephpaulcohen @edreisMD @ebtsai @dr_andrewj Cameron @iScienceLuvr Sergios @curtlanglotz
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