人工智能生成的物质使用和恢复图像:混合方法案例研究
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.
JMIR AI
|February 19, 2026
概括
用于健康沟通的生成人工智能图像可以使耻辱感延续,特别是在物质使用障碍方面. 虽然指南可以改善描述,但代对于公正和准确的表现至关重要.
科学领域:
- 人工智能在健康沟通中的作用
- 医学成像和可视化 医学成像和可视化
- 公共卫生信息学 公共卫生信息学
背景情况:
- 生成型人工智能 (AI) 工具为健康传播提供了可访问,快速和经济有效的图像创建.
- 然而,人工智能产生的视觉图像存在风险,包括对边缘化群体的耻辱.
- 在卫生沟通中使用人工智能需要仔细考虑道德影响和潜在的偏见.
研究的目的:
- 分析通过ChatGPT生成的物质使用障碍 (SUD) 和恢复图像.
- 调查默认视觉输出和迅速修改对减轻污名化图像的影响.
- 评估指导方针信息提示在创建公正的健康传播视觉效果的有效性.
主要方法:
- 一个混合方法案例研究利用ChatGPT 4.o生成了84张与物质使用和恢复相关的图像.
- 图像生成采用了各种提示:口语/污名化,第一人称语言,人工智能生成的,以及基于指南的定制GPT.
- 图像被分析为人口代表和污名化元素使用混合方法的方法.
主要成果:
- 默认的ChatGPT图像主要以白人男性为主 (81%),并描绘了注射毒品使用和链条等耻辱元素.
- 即使使用了第一人称语言,污名化趋势仍然存在.
- 根据指导方针的定制GPT图像不那么污名化,但过度代表黑人女性 (74%).
结论:
- 人工智能生成的物质使用障碍图像反映了现有的偏见和耻辱.
- 提供明确的指导方针可以提高AI图像质量,减少耻辱感.
- 提示和指导方针的代改进对于创建准确和公平的健康传播视觉效果至关重要.
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