A team of scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Jameel Clinic (J-Clinic) demonstrated a deep learning system to predict cancer risk using just a patient’s mammogram. The model showed significant promise and even improved inclusivity: It was equally accurate for both white and Black women, which is especially important given that Black women are 43 percent more likely to die from breast cancer.
Discover, engage, and build at Building for Digital Health 2021 from February 1st-7th. The free, virtual event features a series of 90-min tech talks and a 3-day hackathon. Learn from Google Cloud engineers and discover the capabilities of open source frameworks and Cloud infrastructure that enable advancements in medicine. Organized by MIT Hacking Medicine in partnership with Google Cloud and supported by Apple Health.
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?
CSAIL Alliances and McDermott Will & Emery LLP (MWE) hosted a discussion on contact tracing featuring CSAIL’s Daniel Weitzner, McDermott Will & Emery’s Laura Jehl, and other industry speakers.
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.
