The official handle for #NVIDIAHealthcare. Helping the scientific and developer community advance research, diagnostics, and patient care with #AI.
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NVIDIA Healthcare retweeted
How is AI accelerating scientific progress?
On October 7, I'm moderating a conversation with three founders and leaders working on that question:
• Michelle Lee, CEO of @medra_ai
• Andy Beam, CTO of @LilaSciences
• Gleb Kuznetsov, CEO of @ManifoldBio
@AnthropicAI and @nvidia are bringing together founders, researchers, and builders at Claude Founder House during #SFTechWeek to talk about AI for science and drug discovery.
We’ll start with a short panel, then continue the conversation over drinks. If you’re building in this space, we’d love to see you there.
October 7 · 5–7:30pm PDT · San Francisco
Request to join: luma.com/claude-ai-for-scien…
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Registrations are closing soon! Make sure you get your spot! scverse.org/conference2026/
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Most drugs work like a key in a lock. But hard-to-treat diseases often lack a lock to begin with, leaving them out of reach for conventional medicine.
Covalent drugs change the game by bonding permanently to their targets. To find the right molecules, @ExpeditionMedicines fine-tuned the AIMNet2 model with the NVIDIA ALCHEMI Toolkit—screening 15 million structures per $1,000 of compute (17x faster and cheaper than GFN2-xTB).
expeditionmedicines.com/news…
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This has been a huge week for #MedTech research.
Coming off the heels of @ProjectMonai hitting 10 million downloads, we've dropped some new research⬇️
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A correct answer is only part of the story for medical AI agents. 🩺🤖
Created with @ucsc, AutoMedBench evaluates the full workflow, from planning and setup to validation, inference, and submission.
6,300+ runs. 96 task combinations. 7 tracks.
Check out the paper 👉 arxiv.org/abs/2606.01961
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Photon-counting CT quality is powerful, but not widely accessible.
Spotlighted at #MICCAI2026, SUMI uses radiologist-validated simulations to generate PCCT-like image quality from conventional CT, without requiring paired patient scans.
On external data, SUMI delivered:
• 15% higher structural similarity
• 20% higher peak signal-to-noise ratio
Read the paper 👉 arxiv.org/abs/2604.07329
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Are you a researcher, developer, or clinician working in medical AI? 👩💻🩺
If so, sign up for MedTech Days, our free two-day virtual event taking place October 27–28: nvda.ws/3VrkKWO
You'll get to explore open models for medical imaging and pathology, simulation and digital twins, and physical AI for healthcare robotics. 🤖
#MedTech #Robotics #OpenModels
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Biology is complex. Scaling AI for it just got faster.
See how NVIDIA BioNeMo and Transformer Engine help accelerate mixture-of-experts training for biological foundation models: nvda.ws/3Tt0mUM
#BioNeMo #DigitalBiology #HealthcareAI
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📣 The Open-H-4D call for proposals is now open!
We’re looking for teams to contribute real or simulated data showing how the heart and lungs move over time. The goal: 20,000 cases to help develop AI models of dynamic physiology.
Academic, startup and industry teams are welcome. Proposals are due Dec. 1, 2026.
Read the call: github.com/open-h/open-h-4d
Submit a proposal: docs.google.com/forms/d/e/1F…
#OpenH4D #MedTech #SurgicalRobotics
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What happens when you put biologists, data scientists, toxicologists, oncologists, and software engineers in the same room and give them agentic tools?
One of my favorite things about hackathons is seeing what happens when you give people new tools and let them just build.
We spent three days in London with @OpenAI, @NVIDIAHealth, @WilbeLABS, and 75 scientists and engineers exploring ChatGPT, Rosalind Workbench, NVIDIA BioNeMo, and agentic AI.
The projects went in completely different directions—from systems to make agentic workflows reproducible, to screening herbicide-resistant plant proteins, to identifying chimeric RNAs as potential drug targets.
One of the biggest takeaways for me was seeing the boundary between scientific and engineering work start to blur. Scientists can use agents to turn ideas into working code much more directly. Engineers can use them to get closer to the domain knowledge behind the workflows they're building.
Both skillsets are critical for the future of discovery. Agents are helping bridge the gap to make it much easier for scientists and engineers to build together.
It's a pretty exciting glimpse of the future.
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And the winners are ....
Congratulations to our #NVIDIAGTC Berlin Golden Ticket winners!
Chosen by our panel of judges @asierarranz @choroukmalmoum @SteveNouri @mervenoyann @itsjohnnynunez and @googlecloud, these six winners will join us in Berlin next month:
🏆 Lucas Hudson
🏆 James Kane
🏆 Devin Nicholson
🏆 Aaron Nowak
🏆 Vladimir Shirokun
🏆 Stefan Trauth
Thanks to everyone who participated and shared your projects - we loved seeing what this community is building with open models.
See you at GTC!
What are you building with open models? Show us what you’re working on and you could be headed to #NVIDIAGTC Berlin. 🎫
Your Golden Ticket includes:
→ Free conference pass
→ VIP seating for Jensen’s keynote
→ Exclusive NVIDIA merch
→ Access to special events
Submissions are open Aug 18 – Sep 10, 2026.
Details and how to enter: nvda.ws/4wFdunb
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From full 3D CT volumes to structured findings - with transparent, step-by-step reasoning.
NV-Reason-CT is an open research foundation for developers building 3D, chain-of-thought enabled medical-imaging AI.
Technical Blog: nvda.ws/4dJnvZP
Research Paper: arxiv.org/abs/2609.27511
Hugging Face: huggingface.co/nvidia/NV-Rea…
GitHub: github.com/NVIDIA-Medtech/NV…
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(3/4) To celebrate... MONAI 1.6 just dropped, providing HyenaNDUNETR for segmentation research, richer evaluation tools, and stronger workflow and data reliability.
Check it out: nvda.ws/4AxoZjD
#MONAI #HealthcareAI #OpenSource
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(4/4) MONAI enables all kinds of imaging development, such as this work by @AlibabaGroup to
open-source a medical AI model that can detect cancer and nearly 150 conditions.
Read the article: nvda.ws/4hSxZZi
Explore the GitHub: nvda.ws/3VVeDKt
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(1/4) 10M DOWNLOADS!! 🎉
A huge thank you to the global community for advancing open, interoperable AI for medical imaging and making @ProjectMONAI a cornerstone in that effort.
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(2/4) So many have contributed to this mission: @JohnsHopkins, @UWMadison, @NHSuk, @NIH, @ZJU_China, @MayoClinic, @ChildrensPhila, @CincyChildrens, @MassGenBrigham, @MSKCancerCenter, @MountSinaiNYC, @SiemensHealth, @Philips, @aidocmed, @Kitware, @GSK, @bmsnews, @awscloud, @HonHai_Foxconn, @databricks, @AlibabaGroup, UNSW Sydney, University of Zurich
And more than we can possibly mention
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Introducing MONAI Physio at #MICCAI2026.
3D and 4D medical images ➡️ personalized cardiac and respiratory digital twins.
This open-source Project MONAI toolkit lets researchers model how the heart beats and lungs move for simulation, visualization, and reproducible research.
Explore the project 👉 nvda.ws/3VLmJFq
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