microsoft, and particularly azure, don’t get nearly enough credit for the stuff openai launches. they do an amazing amount of work to make it happen; we are deeply grateful for the partnership. 🙏 they have built by far the best AI infra out there.

Microsoft partnership has been one of OpenAI's secrets to success — we work very closely with Azure to produce AI training & serving infrastructure that can scale to our cutting-edge (& entirely unprecedented!) needs.

🔥 Our new preprint on biomedical self-supervised learning with images only is out! 🔥 Rᴀᴅ-DINO: Exploring Scalable Medical Image Encoders Beyond Text Supervision by Microsoft Research We show that: 0/3 🧵

Due in part to the large number of runaway takes in medical AI, I'm launching a regular thread to spotlight noteworthy and more leveled research papers in the space and/or related fields with promising applications in healthcare. RTs appreciated. Let's begin!

Excited to share our open-source code for cancer survival prediction using radiology (MRI) and pathology (H&E) images - this walkthrough uses our lightweight domain specific multimodal medical imaging embedding models + adapters to produce hazard scores and survival

We have seen many ways that generative AI can transform healthcare but over-stretched health-IT teams can’t deploy new software systems to surface the latest advancements fast enough. But... what if developers could meet clinicians where they already work today, building,

1/🧵 Introducing CancerGUIDE: Cancer Guideline Understanding via Internal Disagreement Estimation. Can LLMs give guideline-concordant cancer treatment advice in data-sparse settings? 🏥Our new framework tests LLMs on guideline adherence in real cancer cases. 👇 #AIinHealthcare