🔥The 9th Round of Easy Loan, Earn $40 Reward is in progress❗️ ⏰ Promotion Period: January 15th - Feburary 15th, 2025 👉 Register now and check more details at gate.io/campaigns/358

𝐒𝐡𝐨𝐮𝐥𝐝 𝐭𝐡𝐞 𝐅𝐃𝐀 𝐚𝐩𝐩𝐫𝐨𝐯𝐞 𝐀𝐈 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬? Let's review using sepsis as a case study 🔥 The U.S. FDA reviews AI/ML tools under the "software as a medical device" program but acknowledges it was not designed for this 🧵 (1/6) 👉 fda.gov/medical-device…

Reminder: Abstract submissions due next Friday 1/17 for #SAIL25 May 6–9 in Río Grande, Puerto Rico! In-person attendance limited to those with accepted work. Join the discussion on integrating AI into clinical medicine. sail.health/event/sail-202…

Emily Alsentzer Outstanding paper — couldn’t agree more! @VUMC_VCLIC is always happy to partner with researchers on real-world evaluations — we need to learn how LLMs actually work in the hospital and clinics!

To move forward as a field, clinicians need to design benchmarks that better align with the tasks LLMs are expected to perform upon deployment. Read the editorial by Deb Raji, BAS, Roxana Daneshjou MD/PhD, MD, PhD, and Emily Alsentzer, PhD: nejm.ai/3PK8v1D

Dr. Emily Alsentzer, a Stanford University faculty member and expert in clinical AI, discusses the evolution of natural language processing, the challenges of AI in clinical settings, and what the future holds for open-source medical AI. Full episode: nejm.ai/4gOGeSo

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!

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?

7/🧵Thanks to my many collaborators on this project, including Hejie Cui, Bowen Chen, Nigam Shah, Emily Alsentzer, Sanmi Koyejo Jason Alan Fries as well as feedback from @bedisuhana42170, @michaelwornow and the teams at Stanford Trustworthy AI Research (STAIR) Lab and the Shah Lab.

#SAIL2025 was a terrific meeting and this sample of anonymized quotes collected by Irene Chen includes a level of candor rarely heard in medical AI meetings (kudos to Emily Alsentzer Isaac Kohane and organizers for both the program and deciding on Chatham House Rule)

In the absence of federal action, the states have led the way for years on pushing forward common sense AI policy. This is really not good.

🚨 Calling all health AI founders & builders! Join us at Health AI Builders: A CHIL Unconference — June 25th @ UC Berkeley. 💡 Real talk on AI, regulation, GTM, & fundraising 👥 Small-group convos, big impact 🎯 Apply to attend: lu.ma/2arsxv64 #CHIL2025 #HealthAI #ML4H

Packed house at #AIMI25 for this star-studded panel featuring Emily Alsentzer (Stanford CS), Karan Singhal (OpenAI), @KhaledSaab11 (DeepMind) and Marinka Zitnik (Harvard Biomedical Informatics) and Ethan Goh 🎯: foundational model roadmap for health AI

🚨 Machine Learning for Health (ML4H) is back and better than ever! 🌴 Join us in San Diego on December 1–2, 2025, right before NeurIPS Conference! 📢 Call for Papers is LIVE → ahli.cc/ml4h/call-for-… ⏳ Deadline: Sept 8 — don’t miss it! 🔁 RT and follow ML4H for updates!

General LLMs perform well on clinical NLP tasks, even though they never see EHR data. How? Our CHIL 2025 paper, led by Furong Jia, investigates by probing the clinical content in pre-training datasets behind popular LLMs. 🔗 arxiv.org/abs/2505.15024

How might AI supercharge world-class expertise in medicine for everyone, everywhere? We’re privileged to pursue this mission Google DeepMind with incredible teammates and are growing our team. We’re hiring stellar Software Engineers, Research Engineers, and Research Scientists

🧵1/Today, we published a key milestone towards AI based cardiac screening in Nature. doi.org/10.1038/s41586… EchoNext outperformed cardiologists and found thousands of high-risk patients missed in routine care. We also made a version available to the world.

#CitationSpotlight on the 2025 npj Digital Medicine paper by Emily Alsentzer, Michelle M. Li (李敏蕊) et al., "Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases" EHR-integrated PhenoTips was used for structured HPO capture nature.com/articles/s4174…