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

Boltz-2 — Towards Accurate and Efficient Binding Affinity Prediction

Learn more

MIT Jameel Clinic and Massachusetts General Hospital researchers selected by ARPA-H ADAPT Program to pioneer revolutionary cancer treatment  

Learn more

The Sybil Implementation Consortium Mission

Learn more

AI doesn’t know ‘no’ – and that’s a huge problem for medical bots

Toddlers may swiftly master the meaning of the word “no”, but many artificial intelligence models struggle to do so. They show a high fail rate when it comes to understanding commands that contain negation words such as “no” and “not”.

That could mean medical AI models failing to realise that there is a big difference between an X-ray image labelled as showing “signs of pneumonia” and one labelled as showing “no signs of pneumonia” – with potentially catastrophic consequences if physicians rely on AI assistance to classify images when making diagnoses or prioritising treatment for certain patients.

It might seem surprising that today’s sophisticated AI models would struggle with something so fundamental. But, says Kumail Alhamoud at the Massachusetts Institute of Technology, “they’re all bad [at it] in some sense”. Learn more

Transforming healthcare at the 2025 InterSystems Middle East Healthcare Summit

Learn more

Study shows vision-language models can’t handle queries with negation words

Imagine a radiologist examining a chest X-ray from a new patient. She notices the patient has swelling in the tissue but does not have an enlarged heart. Looking to speed up diagnosis, she might use a vision-language machine-learning model to search for reports from similar patients.

But if the model mistakenly identifies reports with both conditions, the most likely diagnosis could be quite different: If a patient has tissue swelling and an enlarged heart, the condition is very likely to be cardiac related, but with no enlarged heart there could be several underlying causes.

In a new study, MIT researchers have found that vision-language models are extremely likely to make such a mistake in real-world situations because they don’t understand negation — words like “no” and “doesn’t” that specify what is false or absent. Learn more

15 must-see posters & presentations at ICLR 2025

Learn more

Arianna Huffington and Regina Barzilay discuss the future of health at the Museum of Science

Learn more

New method assesses and improves the reliability of radiologists’ diagnostic reports

Due to the inherent ambiguity in medical images like X-rays, radiologists often use words like “may” or “likely” when describing the presence of a certain pathology, such as pneumonia.

But do the words radiologists use to express their confidence level accurately reflect how often a particular pathology occurs in patients? A new study shows that when radiologists express confidence about a certain pathology using a phrase like “very likely,” they tend to be overconfident, and vice-versa when they express less confidence using a word like “possibly.”

Using clinical data, a multidisciplinary team of MIT researchers in collaboration with researchers and clinicians at hospitals affiliated with Harvard Medical School created a framework to quantify how reliable radiologists are when they express certainty using natural language terms. Learn more
Load More