AI in Clinical Science - amazing data being presented today by @jason-fries.bsky.social Sylvia Plevritis @roxanadaneshjou.bsky.social @akshay-chaudhari.bsky.social but still feel like we are just barely cracking the egg in this field. So impatient for the omelette…! @stanford-cancer.bsky.social

🎉 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. As LLMs enter the clinic, we need rigorous, source-grounded tools to measure what they get right (and wrong).

🎉 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...

[1/4] 🎉 We're thrilled to announce the general release of three de-identified, longitudinal EHR datasets from Stanford Medicine—now freely available for non-commercial research use worldwide! 🚀 Learn more on our HAI blog: hai.stanford.edu/news/advanci...

[1/4] Excited to share that our paper 𝘛𝘪𝘮𝘦-𝘵𝘰-𝘌𝘷𝘦𝘯𝘵 𝘗𝘳𝘦𝘵𝘳𝘢𝘪𝘯𝘪𝘯𝘨 𝘧𝘰𝘳 3𝘋 𝘔𝘦𝘥𝘪𝘤𝘢𝘭 𝘐𝘮𝘢𝘨𝘪𝘯𝘨 is accepted at ICLR 2025! 🚀 We introduce 𝗧𝗧𝗘 𝗽𝗿𝗲𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴, using EHR-linked imaging to improve AI-driven prognosis—essential for assessing disease progression. 🔗 Paper: arxiv.org/abs/2411.09361

Excited to share our paper "Time-to-Event Pretraining for 3D Medical Imaging" is accepted at ICLR 2025! 🚀 We introduce time-to-event pretraining for imaging, leveraging longitudinal EHRs to provide temporal supervision and enhance disease prognosis performance. 🔗 Paper: arxiv.org/abs/2411.09361