What does an AI-generated image of addiction look like, and why does it so often default to darkness, isolation, and despair? As AI tools make it easier than ever to produce visuals for health education, those same tools can unintentionally reinforce stigma about substance use disorder.
In this episode, we explore how AI image generators shape the way addiction is portrayed. Laura brings the perspective from emergency medicine and digital health, where substance use disorder is part of everyday clinical reality and where language and imagery can influence how patients are perceived. Vasanth breaks down the technical side, explaining how diffusion models create images by gradually denoising noise into structured visuals, guided by text prompts that steer what the model produces.
That process is powerful, but it also means biases from internet training data and the connotations embedded in words can compound. The result? AI outputs that repeatedly frame addiction through dramatic “rock bottom” scenes, lone figures, and visual cues that unintentionally reinforce shame rather than understanding.
We also look at research that systematically tests prompts and applies best-practice guidelines for more respectful depictions. The difference is striking: fewer stigmatizing signals, more human-centered imagery, and practical guardrails such as avoiding drug paraphernalia and moving beyond the isolated, ashamed figure. But sanitization has a price. For healthcare AI teams, the lesson is clear: visuals should be treated like clinical content, not decoration, with thoughtful review processes that protect dignity and support stigma-free health communication.
Reference:
AI-Generated Images of Substance Use and Recovery: Mixed Methods Case Study
Heley et al.
JMIR AI (2026)
Credits:
Theme music: Nowhere Land, Kevin MacLeod (incompetech.com)
Licensed under Creative Commons: By Attribution 4.0
https://creativecommons.org/licenses/by/4.0/
