Yale Internal Medicine Faculty Recognized for Pioneering Diagnostic Innovation

When it comes to medical diagnosis, timing and accuracy can mean the difference between recovery and irreversible harm. For two Yale Department of Internal Medicine faculty members — Snigdha Jain, MD, MHS, and Bubu Banini, MD, PhD — getting the diagnosis right, and getting it early, is an important clinical goal. Their commitment to improving diagnostic approaches has earned them national recognition as 2025 Scholars in Diagnostic Excellence by the National Academy of Medicine (NAM).

The NAM Scholars in Diagnostic Excellence program identifies health professionals with the potential to advance diagnostic excellence and reduce diagnostic errors at the national level. Each scholar receives funding, mentorship, and the opportunity to develop a project that will advance equity, accuracy, and systems change in clinical diagnosis.

For Jain, assistant professor of medicine (pulmonary, critical care and sleep medicine), the recognition validates years of research on a little-recognized but serious problem in intensive care units (ICUs): prolonged sedation and ventilator use.

“Up to three-quarters of patients who survive hospitalization with a stay in the ICU develop new and persistent decline in their physical, cognitive, or mental health,” she says. “This decline is a consequence of both critical illness itself and treatments we use in the ICU like sedation and prolonged ventilator support that predispose patients to delirium and immobility.”

Jain's NAM project aims to reduce this harm by using electronic health records (EHRs) to process complex clinical data and alert ICU clinicians when patients are ready to be weaned from sedative medications and ventilator support. There are protocols for this, Jain notes, but they require cognitive bandwidth from already busy clinicians. “I hope my project helps deliver timely, evidence-based care that reduces mortality and long-term complications,” she says. “I also hope this systems-based approach will reduce disparities in care delivery and lead to equitable improvement of long-term outcomes.”

Banini, assistant professor of medicine (digestive diseases), is focused on liver disease, another area where missed or delayed diagnoses often result in irreversible harm. Her project will use artificial intelligence to detect early signs of steatotic liver diseases — such as metabolic dysfunction-associated steatotic liver disease, alcohol-related liver disease, or mixed forms comprising both metabolic dysfunction and alcohol-related liver disease — using data already embedded in patient health records.

“Many people living with liver disease don’t get diagnosed until it’s too late,” says Banini, who is a member of Yale Cancer Center. “Steatotic liver diseases often occur at the crossroads of common risk factors such as excess weight, diabetes, hypertension, or alcohol use and are often asymptomatic until advanced stages.”

Banini believes AI tools can bridge this diagnostic gap by flagging at-risk patients sooner, especially those in underserved communities. “The goal is to create a system that can be widely used to catch liver disease earlier — especially in younger people, women, and those living in underserved communities, who are often overlooked or diagnosed late,” she says.

Banini views her project as part of a broader shift in the use of AI in medicine. “In the coming decade, I see AI reshaping diagnostic medicine by helping us connect complex and ever-growing datasets, allowing us to detect disease earlier and treat it more effectively.”

The Department of Internal Medicine at Yale School of Medicine is among the nation's premier departments, bringing together an elite cadre of clinicians, investigators, educators, and staff in one of the world's top medical schools. To learn more, visit Internal Medicine.