cs.HCApr 14, 2026

Understanding Stigmatizing Language in Clinical Documentation: A Paired Comparison of Ambient AI Drafts and Clinician Finalized Notes

Authors: Yiliang Zhou, Yawen Guo, Sairam Sutari, Jasmine Dhillon, Alexandra L. Beck, Emilie Chow, Steven Tam, Danielle Perret, +4 more

Organizations: Department of Informatics, University of California, Irvine, Irvine, CA, USA · Institute for Clinical and Translational Science, University of California, Irvine, Irvine, CA, USA · Department of Radiological Sciences, University of California, Irvine, Irvine, CA, USA · Department of Medicine, University of California, Irvine, Irvine, CA, USA · Department of Physical Medicine & Rehabilitation, University of California, Irvine, Irvine, CA, USA · Department of Public Health, California State University Fullerton, Fullerton, CA, USA

Abstract

Ambient artificial intelligence (AI) documentation tools are increasingly deployed to reduce clinician documentation burden, but their implications for biased language in clinical notes remain unclear. We conducted a large-scale comparison analysis of AI drafts and corresponding clinician finalized notes to quantify stigmatizing language changes pre- and post-editing. Using a lexicon-based natural language processing (NLP) pipeline, we measured (1) the prevalence of stigmatizing language in AI drafts, (2) the prevalence and term composition in final notes, and (3) the frequency of removal or introduction of stigmatizing terms. Across 66,297 paired note sections, 21.4% of AI draft sections contained at least one stigmatizing language mention, rising to 24.0% in clinician finalized versions. Introductions occurred more often than removals, suggesting clinician editing can be a net source of stigmatizing language entering the EHR with using Ambient AI.

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