🔥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

As clinicians spend more time reading/writing notes, Sharon Jiang, Barbara Lam, Monica Agrawal, Shannon Shen, Nicholas Kurtzman, Steven Horng, David Karger (hci.social/@karger), & David Sontag propose an ML approach that predicts relevant clinical notes in JAMIA academic.oup.com/jamia/advance-…

There is an incredible amount of information about our world hidden in text data. But going from text data to discovery is long, tedious and expensive. Can we do better? tldr; NATURAL is a end-to-end pipeline that goes from raw-text to causal effect estimates.

#Menopause impacts half the world, but there’s still so much we don’t know! Let’s change that. Join us next week at our NSF-NIH workshop to see how AI can crack the code on menopause and transform women’s health. Register for the live stream on Sep 4. new.nsf.gov/events/using-a…

#AI4menopause workshop is starting! Very excited for two days of brainstorming with AI and Menopause researchers ⁦U.S. National Science Foundation⁩ ⁦NIH⁩ ⁦NIH Women's Health⁩

I mean no disrespect to any of the authors in this thread but the claims being made are wild and/or taken out of context. The AgentClinic dataset is based on an online dataset (read contamination) I helped create, from a resource aimed at helping medical students study for the

🚀Announcing the 6th Annual Conference on Health, Inference, and Learning (CHIL 2025) to be held in Berkeley, CA! 🌁🎉 Led by General Chairs Matthew McDermott & Irene Chen, and Program Chairs @drjessilyn & Roxana Daneshjou MD/PhD, this year's event is set to be incredible! 🧵(1/2)

We recently updated our paper on augmenting clinical notes with LLMs to make them more readable. This paper is a great read for thinking how LLMs can improve patient experience (and potential risks!). arxiv.org/abs/2401.09637

🧵When should LLMs trust external contexts in RAG? New paper from Yukun Huang and Sanxing Chen enhances LLMs’ *situated faithfulness* to external contexts -- even when they are wrong!👇

The internet has been progressively diluted with AI-generated slop. Are medical records headed for the same fate? 🧵 I just published a perspective in NEJM with Adam Rodman and Arjun Manrai on why rushing AI into medical documentation could be a mistake.

My lab at Duke has multiple Ph.D. openings! Our mission is to augment human decision-making by advancing the reasoning, comprehension, and autonomy of modern AI systems. I am attending #emnlp2024, happy to chat about PhD applications, LLM agents, evaluation etc etc!

Interested in using LLMs to automate writing clinical notes? Remember stories like this, which happen all the time. Writing = thinking. LLMs can and do help us think, but not if they are implemented thoughtlessly.

🧵LLMs are great at synthesizing info, but unreliable at citing sources. Search engines are the opposite. What lies between them? Our new paper runs human evals on 7 systems across the✨extractive-abstractive spectrum✨for utility, citation quality, time-to-verify, & fluency!

I'm recruiting PhD students for Fall 2025! CS PhD Deadline: Dec. 15th. I work on safe/reliable ML and causal inference, motivated by healthcare applications. Beyond myself, Johns Hopkins has a rich community of folks doing similar work! Come join us!

Medicine thrives on knowledge, yet clinical vocabularies are fragmented. AI struggles to unify this knowledge, creating a gap in precision medicine. Excited to share unified clinical vocabulary embeddings, a project led by stellar Ruthie Johnson Half of healthcare foundation AI

We have a new review on generative AI in medicine, to appear in the Annual Review of Biomedical Data Science! We cover *over 250 papers* in the recent literature to provide an updated overview of use cases and challenges for generative AI in medicine.

We also consider what new challenges generative AI in medicine might raise: what does patient consent look like with LLMs? Who is liable for algorithmic medical errors? These are open questions; there’s some work to answer this but lots more to be done!

Our article on using LLMs to improve health equity is out in NEJM AI! 85% of equity-related LLM papers focus on *harms*. But equally vital are the equity-related *opportunities* LLMs create: detecting bias, extracting structured data, and improving access to health info.

This is joint work with Divya Shanmugam, Raj Movva, Jon Kleinberg, Monica Agrawal, Mark Dredze, Kadija Ferryman, PhD (she/her), Judy Gichoya, Dan Jurafsky, Pang Wei Koh, Karen Levy, 𝐒𝐞𝐧𝐝𝐡𝐢𝐥 𝐌𝐮𝐥𝐥𝐚𝐢𝐧𝐚𝐭𝐡𝐚𝐧, Ziad Obermeyer, Harini Suresh @harinisuresh.bsky.social, and Keyon Vafa! Freely accessible link here: ai.nejm.org/stoken/default…

The Machine Learning for Healthcare (MLHC) 2025 Call for Papers is out! mlforhc.org/paper-submissi… Pre-submission intent deadline: April 4th, 2025 Full submission deadline (Research and Clinical Abstracts Tracks): April 11th, 2025 Submit via OpenReview: openreview.net/group?id=mlfor…