@inproceedings{dahal-etal-2026-investigating,
    title = "Investigating Stigmatizing Language in Clinical Documentation with Open-Source Large Language Models",
    author = "Dahal, Rajashree  and
      Hosseinpour, Pardis  and
      Kamishetty, Pranithi  and
      Pamulaparthy, Satwik  and
      Tizpaz-Niari, Saeid  and
      Parde, Natalie",
    editor = "Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Roberts, Kirk  and
      Tsujii, Junichi",
    booktitle = "{B}io{NLP} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.bionlp-1.39/",
    pages = "490--501",
    ISBN = "979-8-89176-434-7",
    abstract = "Clinical documentation is essential for patient care, billing, and medical research, but it is subject to entrenched bias. While manual chart reviews can identify such bias, they are labor-intensive and expert-dependent. We introduce and evaluate StigMAD, a Multi-Agent Debate framework leveraging open-source Large Language Models (LLMs) to detect stigmatizing language in clinical documentation. We investigate reasoning (multi-agent debate), self-reflection, and self-consistency within this framework. Extensive experiments on clinical notes and patient summaries demonstrate that our framework provides significant advantages over rule-based and supervised baselines. A domain-specific LLM (MedGemma) achieved its highest performance using the StigMAD reasoning framework, while a general-purpose LLM (Llama) showed superior results with the self-consistency framework. These findings suggest that open-source LLMs, steered by structured prompting and reflective reasoning, can effectively support the scalable auditing of stigmatizing language, marking a critical step toward more equitable clinical NLP systems."
}