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Researchers at MIT and Mass General Brigham have built an AI model that can flag intimate partner violence risk in patients from their medical records.
More than one in three women in the United States will experience intimate partner violence (IPV) at some point in their lives, according to the Centers for Disease Control and Prevention. Many will arrive at hospitals or clinics with injuries, chronic pain, anxiety, and depression. Yet even as they receive care for their immediate symptoms, it's often years before these patients can come forward about what they're going through.
We've been hearing about the "quantified self" for nearly two decades as devices to track our steps have evolved to give us health data that used to require a trip to a clinic and cost thousands of dollars. We explore how that health data actually impacts your life, whether you're walking into your next doctor's appointment or forgetting about the sensor sitting on your wrist.
Researchers have repeatedly warned that women don't feel safe asking for help from their healthcare providers for reasons including fear of the abuser, financial dependence, immigration status, and stigma. The US Preventive Services Task Force recommends routine IPV screening for all women of childbearing age. Yet the CDC estimates that current tools, which rely on self-reporting, capture only a fraction of affected patients.
"IPV often remains invisible within healthcare systems despite repeated patient interactions over many years," said Dr. Bharti Khurana, founding director of the Trauma Imaging Research and Innovation Center at Harvard Medical School and an emergency radiologist at Brigham and Women's Hospital.
Working with a team of researchers at Brigham and Women's Hospital, MIT, and Harvard Medical School, Khurana published a study in March 2026 proposing a way to recognize IPV faster. It's an AI model that scans for patients at elevated risk of partner violence using data already stored in their health records. But before the tool can be deployed in hospitals for clinical use, its creators will have to address some remaining concerns around safety and privacy.
The data needed to identify risk already exists, according to researchers. But questions persist as to whether AI can read it reliably and safely enough to be useful. This is the problem that the team hopes to solve, using a system called AIRS.