研究发现，医生使用AI辅助写病历后，歧视性语言反而增多。
Understanding Stigmatizing Language in Clinical Documentation: A Paired Comparison of Ambient AI Drafts and Clinician Finalized Notes
- 对比AI初稿与医生修改后的病历，用NLP分析歧视性用语变化
- AI初稿有21.4%含歧视语言，最终病历升至24.0%
- 医生修改时更常引入而非删除歧视性词汇，适合关注医疗公平者阅读
环境式人工智能（Ambient AI）文档工具正被广泛用于减轻临床记录负担，但其对病历中偏见性语言的影响尚不明确。我们对66,297个配对的病历片段进行了大规模比较分析，量化了AI初稿与医生最终定稿中歧视性语言的变化。采用基于词典的自然语言处理（NLP）流程，测量了：（1）AI初稿中歧视性语言的出现率；（2）最终病历中的出现率及术语构成；（3）歧视性词汇的新增或删除频率。结果显示，21.4%的AI初稿段落包含至少一条歧视性语言，经医生修改后上升至24.0%。新增情况多于删除，表明医生编辑可能成为歧视性语言进入电子健康记录（EHR）的净来源。
原文摘要 · Abstract (English)
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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