Hong-Yu Zhou
@HongYuZhou14
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(he/his/him) PostDoc @HarvardDBMI | AI for Medicine/Health | PhD from @HKUniversity
Boston, MA
Joined January 2022
RT @pranavrajpurkar: A pleasure to collaborate with Jung-Oh Lee, @HongYuZhou14, @tberzin, @DanielSodickson on rethinking the frontier of mu….
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RT @UCJointCPH: It's no trick! 🎃 Join us 10/31 for the CPH fall seminar series on medical #AI: @pranavrajpurkar PhD presents on "Human-AI….
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RT @bryan_johnson: MRI saved my life. It may save yours too. I’m excited to announce the world's best full-body MRI protocol in partnershi….
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RT @_philschmid: Can @AnthropicAI Claude 3.5 sonnet outperform @OpenAI o1 in reasoning? Combining Dynamic Chain of Thoughts, reflection, an….
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RT @EricTopol: I'm excited to let you know I have a new book coming out next spring. @simonschuster .
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RT @chadbyers: Healthcare feels like a top 3 place to build over the next decade. -- $4.5T US spend//20% of GDP --> huge market.-- up to….
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RT @zakkohane: At least 50% of the variance in common diseases is due to environmental exposures. NEXUS is a first step to the Human Exposo….
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🚀 Catching Misses by Doctors! 🚀.a2z-1 launches: AI tackling one of the most common radiological exams - abdominal-pelvis CT scans. 21 conditions. One AI. Unlimited potential. And it's just the beginning. Ever wondered the vision and story behind? 👇.
It's launch day! 🚀 Announcing a2z Radiology AI and our first product, a2z-1. a2z-1 is an AI that analyzes abdominal-pelvis CT scans and reports to catch potential misses across 21 conditions. Our mission is to create a comprehensive AI safety net for radiology, ensuring no
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RT @dbittermanmd: AI models should be clinically validated to demonstrate benefit and implement effectively! Our clinical trial of AI-assis….
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RT @pranavrajpurkar: Looking forward to it. We’re at an inflection point for radiology Ai, and in my talk, I’ll make the case for the emerg….
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We are transitioning from CXR to CT reporting!.
📢 Introducing HeadCT-ONE: Our new paper addresses a major gap in AI evaluation for radiology—capturing semantic equivalence. Using ontologies, we standardize medical terms, making AI-generated head CT reports more accurately comparable, even when phrasing differs.🧠✨
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RT @Michael_D_Moor: Arguably one of the most underrated skills in academic research (esp in CS/ML/AI world) is to create captivating visual….
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RT @jn_acosta: Are you a radiologist interested in shaping the future of AI in healthcare? At Rajpurkar Lab, we are looking for collaborato….
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RT @EricTopol: Cancer in the young is on the rise worldwide, but the basis for this is not known. A new review covers it well, open-access….
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RT @pranavrajpurkar: ⭐️ Announcing ReXRank, a competition for radiology report generation from Chest X-Rays. Featuring 16 existing competi….
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RT @yuyinzhou_cs: Thanks!! @_akhaliq . 🚨📷🚨 Excited to share our new work "MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranul….
github.com
[ICLR 2025] This is the official repository of our paper "MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranular Annotations for Medicine“ - UCSC-VLAA/MedTrinity-25M
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RT @pranavrajpurkar: Excited to introduce RadGraph2: A new dataset for tracking disease progression in radiology reports! 🏥📊.Key features:….
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RT @curtlanglotz: Great advice for radiology trainees:: . Preparing Radiologists for an Artificial Intelligence–enhanced Future: Tips for T….
pubs.rsna.org
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3. Many evaluation metrics do not perform very well when applied to datasets across domains. Recent LLM-based metrics may provide an answer to this. Representatives: FineRadScore ( and Green (.
arxiv.org
The current gold standard for evaluating generated chest x-ray (CXR) reports is through radiologist annotations. However, this process can be extremely time-consuming and costly, especially when...
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