Regina Barzilay is in the business of patient future-telling. That is, using machine learning AI models to predict disease—including when and how it will strike, along with how it may behave. Barzilay began pursuing this after being diagnosed with breast cancer in 2014. As a patient, she experienced the frustrating uncertainty surrounding individual prognoses. Her questions about treatments were often answered in reference to what happened to the participants of clinical trials, but she felt those answers gave her little information about her individual situation.
As an AI researcher, she knew how to address that uncertainty. “To me it was quite clear,” she says, “That's what machine learning is about.” A decade later, the AI model she and her team built, named MIRAI, is able to detect a patient’s risk of developing breast cancer within five years. By 2025, MIRAI was validated by over 2 million mammograms in 48 hospitals across 22 countries.
And her future-telling continues. In 2024, Barzilay worked on an AI model that estimates the expected effectiveness of candidate flu vaccines by predicting which versions of the flu virus are likely to spread next season. She’s now working on using the same concept on cancer, in order to predict how patients—particularly with advanced cancers—will react to a specific treatment. “We are constantly running behind the disease,” she says. “The idea here is to be able to predict it.” Learn more
Could a misspelled word cause a medical crisis? Maybe, if your medical records are being analyzed by an artificial intelligence system. One little typo, or even the use of an unusual word, can cause a medical AI to conclude there’s nothing wrong with somebody who might actually be quite sick.
It’s a real danger, now that hospitals worldwide are deploying systems that use AI software like ChatGPT to assist in diagnosing illnesses. The potential benefits are huge; AIs can be excellent at spotting potential health problems that a human physician might miss.
But new research from Marzyeh Ghassemi, a professor at the Massachusetts Institute of Technology and principal investigator at MIT Jameel Clinic, also finds that these AI tools are often remarkably easy to mislead, in ways that could do serious harm. Learn more
What challenges does health care still face when implementing AI?
The excitement of ambient scribes and AI chatbots has started to wear off, and health care is running out of quick wins as it looks to construct this "new world."
While AI promises efficiency, health systems will have to put in significant time to unlock its full benefits.
Beyond note-taking tools, there are "very little success stories" of clinically implemented AI, said Dr. Regina Barzilay, distinguished professor of AI & Health in the Department of Computer Science at MIT's School of Electrical Engineering and Computer Science and the AI Faculty Lead at MIT's Jameel Clinic.
She told Newsweek that there are abundant stories of new AI algorithms that show promising results but few that have actually proven themselves beyond mere promise. Learn more
Most computational research in organ allocation is focused on the initial stages, when waitlisted patients are being prioritized for organ transplants. In a new paper presented at ACM Conference on Fairness, Accountability, and Transparency (FAccT) in Athens, Greece, researchers from MIT and Massachusetts General Hospital focused on the final, less-studied stage: organ offer acceptance, when an offer is made and the physician at the transplant center decides on behalf of the patient whether to accept or reject the offered organ.
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When Regina Barzilay was diagnosed with breast cancer in 2014, it upended her life and shifted the direction of her research. Already an accomplished computer scientist specializing in natural language processing, her experience as a patient shed light on the possibility of new applications for machine learning and revealed a stark disconnect between technology’s promise and its implementation in health care. “It was upsetting to see that all these great technologies are not translated into patient care,” she recalls. “I wanted to change it.” After going through her own treatment, Barzilay’s work took on an urgent new focus: could the very technologies she used in her research predict who might be at risk for breast cancer? Learn more
