Author: Alex Ouyang
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
Balancing Power With Caution: AI’s Impact On Breast Cancer
During my seven years as president and CEO of Susan G. Komen, I’ve witnessed the impact of innovation in our mission to end breast cancer for good. Over the past four decades, we’ve seen a 44% reduction in breast cancer mortality, thanks to early detection and better treatments. AI is now accelerating this progress with its capacity to analyze vast datasets, discover new patterns and enhance diagnostic accuracy.Take, for example, the groundbreaking work of Komen scholar Dr. Regina Barzilay. Using her own mammograms in her research at MIT, Dr. Barzilay demonstrated how AI could have detected her breast cancer much earlier, potentially improving her prognosis. Studies show that incorporating AI into mammogram analysis boosts cancer detection rates by 20%, without increasing false positives. This is a significant leap forward, as early detection is key to a better chance at positive outcomes and survival. 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
Using AI to predict lung cancer risk
In recent years, lung cancer rates have been rising in nonsmokers, a troubling trend for the world's #1 deadliest cancer. Sybil is a deep learning model built by MIT Jameel Clinic and Mass General Brigham researchers that accurately predicts lung cancer risk up to 6 years in advance by analyzing a patient's LDCT scan. How exactly does this state-of-the-art model work and what was the key insight that brought it to life? Watch this 7-minute video featuring the researchers behind the model to learn more about how Sybil is transforming the future of lung cancer screening. Learn more
