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Happy to share our review discussing the challenges and opportunities of multimodal biomedical AI! Guido J. Falcone MD, ScD, MPH Pranav Rajpurkar Eric Topol

Practical computational population level genetic course by Guido J. Falcone MD, ScD, MPH at Neurocritical Care Society #NCS2022 with Raj Dhar Julián Nicolás Acosta and AriErcole How to do GWAS, PRS and Mendelian randomisation in R

Esto es una hermosura que nos trajo 🎅🏻

Not everyday a large language model scores 85.4% on the US medical licensing exam (USMLE) Implications erictopol.substack.com/p/multimodal-a…

1/ 📰 Breaking: Forbes covers @RadAI’s partnership with Google, which builds upon our leadership in GenAI reporting and collaboration on the latest #GenAI models like #MedLM, including #GeminiAI based models in the future. forbes.com/sites/saibala/… Key highlights in 🧵

Very insightful study. A must read if you’re in medical AI!

Radiologists, are you ready to shape the future of your field with AI? 10 min application: shorturl.at/fgmSY Join the Medical AI Bootcamp, where you'll have the unique opportunity to embed yourself in an environment that allows you to think deeply about and write a

a reference implementation, no matter how hacky, often provides more clarity than any spec

Just published Our review article of all the medical #AI randomized controlled trials The Lancet Digital Health thelancet.com/journals/landi… Ryan Han Julián Nicolás Acosta Pranav Rajpurkar

🚨 New Multimodal Generative Medical AI 🚨 Our team just announced MedVersa, a groundbreaking multimodal medical image interpretation model. 🩺🖥️ MedVersa is the first generalist medical AI capable of learning from both visual & linguistic supervision to tackle a wide range of

⭐️ Announcing ReXRank, a competition for radiology report generation from Chest X-Rays. Featuring 16 existing competitive approaches, a large private test leaderboard of 10k cases indicative of more real-world performance, and a live arena where radiology experts can do a

How AI might change medical care—Interviews w/DBMI's Isaac Kohane & Pranav Rajpurkar begin 3:03. Bonus note: The mom who diagnosed her own son's tethered cord syndrome w/ChatGPT after 3 years & 17 doctors (3:33) co-keynotes our 10th annual Precision Medicine conference 10/1/24.

💫Excited to share our new study: “Uncovering Knowledge Gaps in Radiology Report Generation Models through Knowledge Graphs”. We’ve developed a system ReXKG to extract structured information from radiology reports, building a comprehensive knowledge graph for in-depth model

Are you a radiologist interested in shaping the future of AI in healthcare? At Rajpurkar Lab, we are looking for collaborators to join us in an exciting upcoming project. If interested, please reach out - DMs are open! Pranav Rajpurkar

Excited to be moving beyond chest X-rays and diving into more complex studies—as a neurologist, working on head CT feels like coming home.

Years ago, as a neuroscience obsessed med student, I came across artificial neural networks. Later, when I first learned about deep learning, I was immediately convinced that it would work, which led me to take very unconventional career paths. Fortunately, deep learning worked.

It's launch day! 🚀 Announcing a2z Radiology AI and our first product, a2z-1. a2z-1 is an AI that analyzes abdominal-pelvis CT scans and reports to catch potential misses across 21 conditions. Our mission is to create a comprehensive AI safety net for radiology, ensuring no

John Hopfield and Geoffrey Hinton, along with collaborators, have created a beautiful and insightful bridge between physics and AI. They invented neural networks that were not only inspired by the brain, but also by central notions in physics such as energy, temperature, system

🚨 AI in Radiology Alert! 🚨 How much can AI help radiologists reduce reporting time while maintaining accuracy? Harvard Medical School & collaborators present a pilot study on AI-assisted radiology workflows using AI-generated draft reports! Authors: Julián Nicolás Acosta, Sid Dogra,

Incredibly proud to share a2z Radiology AI's first research paper evaluating a2z-1. Our AI model detects 21 conditions in abdomen-pelvis CTs with an AUC of 0.931. This represents the most comprehensive external validation of a multi-disease detection AI in abdominal imaging to