Suppose you're a machine-learning researcher trying to build a model that could help plan for the COVID-19 pandemic. You want to incorporate a disease simulator into the model, but it's written in the C++ programming language, rather than an existing machine-learning workflow like PyTorch or TensorFlow. A team from MIT CSAIL recently developed a clever work-around.

One paradox about antibiotics is that, broadly speaking, the more we use them, the less they continue to work. The Darwinian process of bacteria growing resistant to antibiotics means that, when the drugs don't work, we can no longer treat infections, leading to groups like the World Health Organization warning about our ability to control major public health threats.

Modern health care has been reinvigorated by the widespread adoption of artificial intelligence. From speeding image analysis for radiology to advancing precision medicine for personalized care, AI has countless applications, but can it rise to the challenge in the fight against Covid-19?

Machine learning is a computational tool used by many biologists to analyze huge amounts of data, helping them to identify potential new drugs. MIT researchers have now incorporated a new feature into these types of machine-learning algorithms, improving their prediction-making ability.

Scientists working at the intersection of AI and cancer care need to be more transparent about their methods and publish research that is reproducible, according to a new commentary co-authored by CSAIL's Tamara Broderick. 

A group led by researchers at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) has developed a machine learning model that can look at an X-ray to quantify how severe the edema is, on a four-level scale ranging from 0 (healthy) to 3 (very, very bad). The system determined the right level more than half of the time, and correctly diagnosed level 3 cases 90 percent of the time.

MIT researchers have developed a wireless, private way to monitor a person’s sleep postures — whether snoozing on their back, stomach, or sides — using reflected radio signals from a small device mounted on a bedroom wall.

Launched in May, Covid Controls was developed by a team who met while working at the Singapore-MIT Alliance for Research & Technology (SMART), a research center created in 2007 by the Massachusetts Institute of Technology in partnership with the National Research Foundation of Singapore.