Emma Chen

[Pranav Rajpurkar @pranavrajpurkar]

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 imaging modality, enabling MedVersa to optimize for specific tasks and modalities.

This approach, combined with the dataset's richness, was key to developing MedVersa's versatility and robustness. 💪📊

[Hong-Yu Zhou @HongYuZhou14]

We announced MedVersa, a generalist AI that excels in multifaceted medical image interpretation! 🚀🩺

👉https://t.co/AnibKW0rdO

MedVersa has two promising features🧐:

1. Learning from vision and language supervision. This maximizes the flexibility of the framework. Imagine combining the SAM and CLIP into a unified model and the mutual benefits it will bring!🤪

2. Leveraging existing modules/tools. MedVersa functions the LLM as a TRAINABLE orchestrator. This design features a notable level of extensibility, allowing integration with advanced modules/tools.🤩

A great journey with @subathraadithan and @jn_acosta
, led by @EricTopol and @pranavrajpurkar

Stay tuned for more updates!👀

#artificiallyinteligence

[Pranav Rajpurkar @pranavrajpurkar]

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.

https://t.co/yyF3Jixiil
https://t.co/jvfIh6aN0c

[Eric Topol @EricTopol]

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
https://t.co/ZcviaWRPN5
w/ @emma_ychen and @pranavrajpurkar

[Pranav Rajpurkar @pranavrajpurkar]

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.
🧵
https://t.co/jvfIh6bkPK

[Pranav Rajpurkar @pranavrajpurkar]

Key deployment consideration for AI + medicine

Can we predict when AI models for medical image diagnosis will make incorrect predictions?

New paper (#CheXbreak):
https://t.co/AwKeF3pKD5

w/ @emma_ychen, Andy Kim, @RayanKrishnan, @JinLong19844963 @AndrewYNg @StanfordAILab

1/n