Key deployment consideration for AI + medicine Can we predict when AI models for medical image diagnosis will make incorrect predictions? New paper (#CheXbreak): arxiv.org/abs/2103.09957 w/ Emma Chen, Andy Kim, Rayan Krishnan, Jin Long Andrew Ng Stanford AI Lab 1/n

Medical data used to train AI algorithms lacks diversity. Most data comes from a few hospitals in the US. We are launching an initiative to bring you diverse medical image datasets from across the globe: MAIDA. Here's what MAIDA is about. 🧵 rajpurkarlab.hms.harvard.edu/maida

Every week, for the past 3+ years, we summarize the important #AI papers in medicine, life science, and healthcare. It's free to subscribe and we've just moved to over Substack doctorpenguin.substack.com w/ Emma Chen and Pranav Rajpurkar

Are you passionate about advancing global healthcare and save lives through data? We're hiring for a full-time role at Harvard where you will play a pivotal role in expanding our international data-sharing coalition. tinyurl.com/y6d5f8zy rajpurkarlab.hms.harvard.edu/maida

One of the coolest aspects of this work is how we leverage the MedInterp dataset. Its diversity across 11 tasks and 3 modalities allowed us to train MedVersa in a unique way. By using domain-aware minibatch gradient descent, we constructed minibatches from the same task and

We announced MedVersa, a generalist AI that excels in multifaceted medical image interpretation! 🚀🩺 👉arxiv.org/abs/2405.07988 MedVersa has two promising features🧐: 1. Learning from vision and language supervision. This maximizes the flexibility of the framework. Imagine