Purpose <p>Cancer survivorship begins at diagnosis and encompasses a wide variety of experiences, yet prominent societal narratives of survivorship emphasize a positive, post-treatment “return-to-normal.” These representations shape how survivorship is understood and experienced by cancer survivors and the public. This study aimed to (1) characterize artificial intelligence (AI)–generated images of cancer survivors and (2) compare them to images of cancer patients to understand how these images might reflect and amplify prevalent survivorship narratives.</p> Methods <p>Two AI text-to-image tools (<i>DALL-E</i>, <i>Stable Diffusion</i>) were prompted to generate 40 images each of cancer survivors and cancer patients (<i>n</i> = 160 images). Images were coded for perceived demographics, affect, health, markers of illness or cancer, and setting. Chi-square analyses tested differences between images of cancer patients and survivors. Quantitative data were complemented by coders’ qualitative insights.</p> Results <p>Cancer survivors in AI-generated images were largely perceived as White (80%), feminine (80%), young (51%), happy (69%), and healthy (80%), and many images were observed to conform to Western beauty ideals. Pink (64%), cancer ribbons (35%), and head scarves (51%) were prominent visual features in survivor images. Compared to images of cancer patients, survivor images more frequently featured individuals perceived as non-White (<i>p</i> = .03), young (<i>p</i> &lt; .001), affectively positive (<i>p</i> &lt; .001), and healthy (<i>p</i> &lt; .001), and less frequently included markers of illness like portraying individuals in bed (<i>p</i> &lt; .001) or in medical settings (<i>p</i> &lt; .001).</p> Conclusions <p>AI-generated images of cancer survivors fail to reflect the breadth of survivor demographics or experience.</p> Implications for Cancer Survivors <p>AI-generated images may perpetuate narrow views of cancer survivorship.</p>