Super excited to join Columbia DBMI this coming July! If you're looking for postdoctoral or PhD opportunities in Health AI (in particular in building foundation models for EHR and multimodal health data), message me!

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

While we celebrate DeepSeek 's release of open-weight models that we can all play with at home, just a friendly reminder that they are not *open-source*; there’s no training / data processing code, and hardly any information about the data.

Excited to share that our paper "Time-to-Event Pretraining for 3D Medical Imaging" has been accepted to ICLR 2025! 🚀 Electronic health records (EHRs) contain a wealth of longitudinal data on disease progression. In this work, we use methods from survival analysis to transform

Some new work from our group that I'm very excited about! What makes weak-to-strong generalization possible? We think it's all about data!

Also, please follow me on BlueSky bsky.app/profile/jason-…

OpenAI's Health AI team is now hiring backend/fullstack SWEs towards our mission of universalizing access to health information! Please apply if you: - Can write maintainable, high-quality backend / fullstack code at high velocity - Are willing to run through walls towards this

1/🧵How do we know if AI is actually ready for healthcare? We built a benchmark, MedHELM, that tests LMs on real clinical tasks instead of just medical exams. #AIinHealthcare Blog, GitHub, and link to leaderboard in thread!

Can AI in healthcare truly be responsible without full patient histories? New longitudinal EHR datasets provide a better way to benchmark models. Read more from #StanDOM's Jason Alan Fries, Zepeng Frazier Huo, Hejie Cui, Nigam Shah & Shah Lab colleagues. stanford.io/41lLvg0

1/🧵Introducing TIMER: Temporal Instruction Modeling and Evaluation for Longitudinal Clinical Records When we evaluate LLMs for reasoning over longitudinal clinical records, can we leverage synthetic data generation to create scalable benchmarks and improve model performance?

Reminder: COLM abstract deadline! Should be an amazing conference this year in Montreal.

!!! I'm at #ICLR2025 to present 🧄Aioli🧄 a unified framework for data mixing on Thursday afternoon! 🔗 arxiv.org/abs/2411.05735 Message me to chat about pre/post training data (mixing, curriculum, understanding); test-time compute/verification; or to try new food 🇸🇬

Today at #ICLR2025---come chat with Changho Shin about our work on what types of data drive weak-to-strong generalization!

🎉 Excited to present our #ICLR2025 work—leveraging future medical outcomes to improve pretraining for prognostic vision models. 🖼️ "Time-to-Event Pretraining for 3D Medical Imaging" 👉 Hall 3+2B #23 📍 Sat 26 Apr, 10 AM–12:30 PM 🔗 iclr.cc/virtual/2025/p…

Excited to present this work at ICLR's SynthData Workshop on Sunday April 27! Come through from 11:30-12:30 @ Peridot 202-203 to talk anything synthetic data for post-training, benchmarking, and AI for healthcare in general.

A delightful Sunday at #ICLR2025 in the Pediatric AI workshop pediamedai.com/ai4chl/ listening to an exciting talk by Jason Alan Fries describing his exciting work with Nigam Shah Stanford Health Care and others!

Amazing work by Snorkel AI —scaling domain expertise for evaluation and data curation is key to unlocking AI’s potential in high-stakes fields like healthcare. So excited for what’s next! 🚀

🎉 Headed to MLHC 2025 this weekend? Swing by Poster #154 (Session C) on Saturday, Aug 16 to check out FactEHR — our new benchmark for evaluating factuality in clinical notes!