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<p>Stigma remains a major barrier to equitable health and well-being, while artificial intelligence (AI) is increasingly recognized as a tool with both potential and risk in addressing this challenge. However, research on AI and stigma is fragmented across disciplines, hindering a unified understanding of their intersection. To consolidate existing evidence, we conducted a scoping review of 11,769 records published between 2016 and 2025 and identified 70 studies examining the relationship between AI and health-related stigma. Four research themes emerged: AI measuring stigma (<i>n</i> = 42, 60%), stigma influencing AI use (<i>n</i> = 15, 21%), AI increasing stigma (<i>n</i> = 9, 13%), and AI reducing stigma (<i>n</i> = 4, 6%). Most studies focused on mental health disorders, revealing an imbalance in attention to other health conditions. Across studies, we observed inconsistent definitions of stigma, limited cross-cultural perspectives, and few evaluations of real-world AI applications. Addressing these gaps will be critical for developing responsible and equitable AI systems that mitigate rather than reinforce health stigma across broader societal and health contexts.</p>

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Mapping the role of artificial intelligence in health-related stigma: a scoping review

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月30日

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  • Tianqi Song,
  • Jack Jamieson,
  • Wataru Akahori,
  • Han Meng,
  • Shuqi Wang,
  • Yi-Chieh Lee

摘要

Stigma remains a major barrier to equitable health and well-being, while artificial intelligence (AI) is increasingly recognized as a tool with both potential and risk in addressing this challenge. However, research on AI and stigma is fragmented across disciplines, hindering a unified understanding of their intersection. To consolidate existing evidence, we conducted a scoping review of 11,769 records published between 2016 and 2025 and identified 70 studies examining the relationship between AI and health-related stigma. Four research themes emerged: AI measuring stigma (n = 42, 60%), stigma influencing AI use (n = 15, 21%), AI increasing stigma (n = 9, 13%), and AI reducing stigma (n = 4, 6%). Most studies focused on mental health disorders, revealing an imbalance in attention to other health conditions. Across studies, we observed inconsistent definitions of stigma, limited cross-cultural perspectives, and few evaluations of real-world AI applications. Addressing these gaps will be critical for developing responsible and equitable AI systems that mitigate rather than reinforce health stigma across broader societal and health contexts.