What does an AI-generated “cancer survivor” look like? An analysis of images generated by text-to-image tools

Abstract

PURPOSE: 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. METHODS: Two AI text-to-image tools (DALL-E, Stable Diffusion) were prompted to generate 40 images each of cancer survivors and cancer patients (n = 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. RESULTS: 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 (p = .03), young (p < .001), affectively positive (p < .001), and healthy (p < .001), and less frequently included markers of illness like portraying individuals in bed (p < .001) or in medical settings (p < .001). CONCLUSIONS: AI-generated images of cancer survivors fail to reflect the breadth of survivor demographics or experience. IMPLICATIONS FOR CANCER SURVIVORS: AI-generated images may perpetuate narrow views of cancer survivorship.

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