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Related Experiment Video

Updated: Feb 21, 2026

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AI-Generated Images of Substance Use and Recovery: Mixed Methods Case Study.

Kathryn Heley1,2, Jeffrey K Hom3,4, Linnea Laestadius1

  • 1Zilber College of Public Health, University of Wisconsin Milwaukee, 1240 N 10th St, Milwaukee, WI, 53205, United States, 1 (414) 251-5607.

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Summary

Generative AI images for health communication can perpetuate stigma, especially regarding substance use disorder. While guidelines improve depictions, iteration is crucial for unbiased and accurate representation.

Keywords:
AI biasSubstance useartificial intelligencegenerative AIhealth communicationimplicit biasstigmavisual communication

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Area of Science:

  • Artificial Intelligence in Health Communication
  • Medical Imaging and Visualization
  • Public Health Informatics

Background:

  • Generative artificial intelligence (AI) tools offer accessible, rapid, and cost-effective image creation for health communication.
  • However, AI-generated visuals pose risks, including perpetuating stigma against marginalized groups.
  • The use of AI in health communication necessitates careful consideration of ethical implications and potential biases.

Purpose of the Study:

  • To analyze images of substance use disorder (SUD) and recovery generated by ChatGPT.
  • To investigate default visual outputs and the impact of prompt modifications on mitigating stigmatizing imagery.
  • To assess the effectiveness of guideline-informed prompting in creating unbiased health communication visuals.

Main Methods:

  • A mixed-methods case study utilized ChatGPT 4.o to generate 84 images related to substance use and recovery.
  • Image generation employed varied prompts: colloquial/stigmatizing, person-first language, AI-generated, and guideline-informed custom GPT.
  • Images were analyzed for demographic representation and stigmatizing elements using a mixed-methods approach.

Main Results:

  • Default ChatGPT images predominantly featured White men (81%) and depicted stigmatizing elements like injection drug use and chains.
  • Even with person-first language, stigmatizing trends persisted.
  • Guideline-informed custom GPT images were less stigmatizing but overrepresented Black women (74%).

Conclusions:

  • AI-generated images for substance use disorder reflect existing biases and stigma.
  • Providing clear guidelines can improve AI image quality and reduce stigma.
  • Iterative refinement of prompts and guidelines is essential for creating accurate and equitable health communication visuals.