Tag: MIT News
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
MIT faculty, alumni named 2025 Sloan Research Fellows
Marzyeh Ghassemi PhD ’17 is an associate professor within EECS and the Institute for Medical Engineering and Science (IMES). Ghassemi earned two bachelor’s degrees in computer science and electrical engineering from New Mexico State University as a Goldwater Scholar; her MS in biomedical engineering from Oxford University as a Marshall Scholar; and her PhD in computer science from MIT. Following stints as a visiting researcher with Alphabet’s Verily and an assistant professor at University of Toronto, Ghassemi joined EECS and IMES as an assistant professor in July 2021. (IMES is the home of the Harvard-MIT Program in Health Sciences and Technology.) She is affiliated with the Laboratory for Information and Decision Systems (LIDS), the MIT-IBM Watson AI Lab, the Abdul Latif Jameel Clinic for Machine Learning in Health, the Institute for Data, Systems, and Society (IDSS), and CSAIL. Ghassemi’s research in the Healthy ML Group creates a rigorous quantitative framework in which to design, develop, and place machine learning models in a way that is robust and useful, focusing on health settings. Her contributions range from socially-aware model construction to improving subgroup- and shift-robust learning methods to identifying important insights in model deployment scenarios that have implications in policy, health practice, and equity. Among other awards, Ghassemi has been named one of MIT Technology Review’s 35 Innovators Under 35 and an AI2050 Fellow, as well as receiving the 2018 Seth J. Teller Award, the 2023 MIT Prize for Open Data, a 2024 NSF CAREER Award, and the Google Research Scholar Award. She founded the nonprofit Association for Health, Inference and Learning (AHLI) and her work has been featured in popular press such as Forbes, Fortune, MIT News, and The Huffington Post. Learn more
Can deep learning transform heart failure prevention?
The ancient Greek philosopher and polymath Aristotle once concluded that the human heart is tri-chambered and that it was the single most important organ in the entire body, governing motion, sensation, and thought.Today, we know that the human heart actually has four chambers and that the brain largely controls motion, sensation, and thought. But Aristotle was correct in observing that the heart is a vital organ, pumping blood to the rest of the body to reach other vital organs. When a life-threatening condition like heart failure strikes, the heart gradually loses the ability to supply other organs with enough blood and nutrients that enables them to function.
Researchers from MIT and Harvard Medical School recently published an open-access paper in Nature Communications Medicine, introducing a noninvasive deep learning approach that analyzes electrocardiogram (ECG) signals to accurately predict a patient’s risk of developing heart failure. In a clinical trial, the model showed results with accuracy comparable to gold-standard but more-invasive procedures, giving hope to those at risk of heart failure. The condition has recently seen a sharp increase in mortality, particularly among young adults, likely due to the growing prevalence of obesity and diabetes. Learn more
MIT researchers introduce Boltz-1, a fully open-source model for predicting biomolecular structures
MIT scientists have released a powerful, open-source AI model, called Boltz-1, that could significantly accelerate biomedical research and drug development. Developed by a team of researchers in the MIT Jameel Clinic for Machine Learning in Health, Boltz-1 is the first fully open-source model that achieves state-of-the-art performance at the level of AlphaFold3, the model from Google DeepMind that predicts the 3D structures of proteins and other biological molecules. MIT graduate students Jeremy Wohlwend and Gabriele Corso were the lead developers of Boltz-1, along with MIT Jameel Clinic Research Affiliate Saro Passaro and MIT professors of electrical engineering and computer science Regina Barzilay and Tommi Jaakkola. Wohlwend and Corso presented the model at a Dec. 5 event at MIT’s Stata Center, where they said their ultimate goal is to foster global collaboration, accelerate discoveries, and provide a robust platform for advancing biomolecular modeling. Learn more
Using AI, MIT researchers identify a new class of antibiotic candidates
Using a type of artificial intelligence known as deep learning, MIT researchers have discovered a class of compounds that can kill a drug-resistant bacterium that causes more than 10,000 deaths in the United States every year. Learn more
How an archeological approach can help leverage biased data in AI to improve medicine
Although computer scientists may initially treat data bias and error as a nuisance, researchers argue it’s a hidden treasure trove for reflecting societal values. Learn more
How to help high schoolers prepare for the rise of artificial intelligence
A one-week summer program aims to foster a deeper understanding of machine-learning approaches in health among curious young minds. Learn more
Generative AI Imagines New Protein Structures
“FrameDiff” is a computational tool that uses generative AI to craft new protein structures, with the aim of accelerating drug development and improving gene therapy. Learn more