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The Catatonia Quick Screen (CQS): A Rapid Screening Tool for Catatonia in Adult and Pediatric Populations.

medRxiv : the preprint server for health sciences·2024
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Related Experiment Video

Updated: May 8, 2026

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
08:53

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Published on: May 31, 2019

Challenges of AI-generated stigmatizing language regarding substance use disorders.

Vincent Kennedy1, Andrew Francis1

  • 1Department of Psychiatry and Behavioral Health, Penn State College of Medicine, Hershey, Pennsylvania, USA.

Journal of Addictive Diseases
|May 6, 2026
PubMed
Summary

Large language models (LLMs) may perpetuate stigmatizing language in clinical documentation due to their training data. Clinicians must carefully review AI-generated content to prevent harm to patients with substance use disorders.

Keywords:
LLM outputSUD AIaddiction stigma

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Last Updated: May 8, 2026

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
08:53

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Published on: May 31, 2019

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Public Health

Background:

  • Stigmatizing language in clinical settings negatively impacts patients with substance use disorders.
  • Existing literature advises against using stigmatizing language in medical documentation and patient education.
  • Large language models (LLMs) trained on diverse datasets, including electronic health records, risk generating output with stigmatizing language.

Purpose of the Study:

  • To identify stigmatizing language related to substance use disorders in LLM output.
  • To explore the potential for LLMs to perpetuate such language in clinical documentation.
  • To discuss challenges clinicians face in mitigating this risk and propose solutions.

Main Methods:

  • Literature review on stigmatizing language in substance use disorder contexts.
  • Analysis of the potential for LLMs to incorporate and propagate stigmatizing language.
  • Discussion of practical challenges and proposed strategies for clinicians.

Main Results:

  • LLMs may inadvertently generate stigmatizing language due to their training data, posing a risk in clinical documentation.
  • Challenges include inherent randomness in LLM output, balancing language use, and the need for human oversight.
  • Diverging priorities between AI developers and clinical needs may hinder the elimination of stigmatizing language.

Conclusions:

  • Clinicians must exercise caution with LLM-generated clinical documentation due to the risk of perpetuating stigmatizing language.
  • Involving practicing clinicians in the iterative refinement of LLM output is crucial to reduce the perpetuation of stigmatizing language.
  • Continued human review of AI-assisted medical records is essential to ensure patient safety and ethical care.