August 06,2025

Health Care AI Solved Note-Taking. Fixing the Core Business Will Be Harder

What challenges does health care still face when implementing AI?

The excitement of ambient scribes and AI chatbots has started to wear off, and health care is running out of quick wins as it looks to construct this "new world."

While AI promises efficiency, health systems will have to put in significant time to unlock its full benefits.

Beyond note-taking tools, there are "very little success stories" of clinically implemented AI, said Dr. Regina Barzilay, distinguished professor of AI & Health in the Department of Computer Science at MIT's School of Electrical Engineering and Computer Science and the AI Faculty Lead at MIT's Jameel Clinic.

She told Newsweek that there are abundant stories of new AI algorithms that show promising results but few that have actually proven themselves beyond mere promise. Learn more

July 15,2025

9 must-see posters at ICML 2025

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June 12,2025

Can AI Predict Breast Cancer? How a Scientist’s Personal Challenge Launched a Professional Mission

When Regina Barzilay was diagnosed with breast cancer in 2014, it upended her life and shifted the direction of her research. Already an accomplished computer scientist specializing in natural language processing, her experience as a patient shed light on the possibility of new applications for machine learning and revealed a stark disconnect between technology’s promise and its implementation in health care. “It was upsetting to see that all these great technologies are not translated into patient care,” she recalls. “I wanted to change it.” After going through her own treatment, Barzilay’s work took on an urgent new focus: could the very technologies she used in her research predict who might be at risk for breast cancer? Learn more

June 06,2025

Boltz-2 Released to Democratize AI Molecular Modeling for Drug Discovery

Researchers from the Massachusetts Institute of Technology (MIT) Jameel Clinic for Machine Learning in Health have announced the open-source release of Boltz-2, which now predicts molecular binding affinity at newfound speed and accuracy to democratize commercial drug discovery. The model is available under the highly permissive MIT license, which allows commercial drug developers to use the model internally and apply their own proprietary data. Learn more

June 06,2025

The Prototype: This AI Model Could Make It Faster To Find New Medicines

The 2024 Nobel Prize in Chemistry was awarded in part to Deepmind’s Demis Hassabis and John Jumper for the development of AlphaFold–an AI model that predicts the structure of proteins, the complex chemicals essential to making our bodies work. Since its inception, this model and others like it have been put to use in laboratories around the world, enabling new biological discoveries.

Now a team from MIT and pharmaceutical company Recursion, with support from Cancer Grand Challenges, have developed a tool that takes these principles further–and may help researchers find new medicines more quickly. Called Boltz-2, this open-source generative AI model can not only predict the structure of proteins, it can also predict its binding affinity–that is, how well a potential drug is able to interact with that protein. This is crucial in the early stages of developing a new medicine. Learn more