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Artificial intelligence (AI)-driven tools used in emergency triage may be susceptible to the same clinical stigmas associated with human assessments, researchers at UT Southwestern Medical Center reported in a study published in npj Digital Medicine. Their findings suggest this could result in harmful biases and wrongful prioritization of patients.

"Wording matters. Because AI models process the words we use to describe patients, stigmatizing language can steer clinicians away from the patients who need care the most," said first author Philip Jarrett, M.D., MBA, assistant professor of emergency medicine. "This study is part of a broader research program on how clinical information should be presented to AI so it can safely help prioritize emergency patients competing for limited resources."

The team analyzed patient records from emergency departments (EDs) at two major academic institutions: Stanford Medicine in California and Beth Israel Deaconess Medical Center in Massachusetts. They paired patients with similar initial condition severity levels, then asked three open-source AI large language models (LLMs) to predict which patients were more likely to deteriorate and who should be prioritized for care. Before demographic, social or potentially stigmatizing information was added, all three models identified higher-risk patients at rates well above chance.

Researchers then added different types of information to patient records—including mental health and substance use history, age and patterns of ED use—and evaluated how the AI models' decisions changed.

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