FAIRFAX, Va., July 10 -- George Mason University issued the following news release:
Large language models can identify judgmental language in clinical notes but the settings play a major role in accuracy.
"Addict," "non-compliant," "failed treatment," and "obese person" are examples of stigmatizing language that can appear in medical records. At George Mason University's College of Public Health, researchers are exploring whether artificial intelligence (AI) can help identify this kind of language in clinical notes before it impacts patient care.
Nurse scientist Teenu Xavier and colleagues found that large language models (LLMs) show promise in identifying stigmatizing language in clinical documentation, but their performance is highly d...