ArticleJournal of cancer survivorship : research and practice2026
What does an AI-generated "cancer survivor" look like? An analysis of images generated by text-to-image tools.
Article in Journal of cancer survivorship : research and practice, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Review
- How Hospice and Palliative Care Are Portrayed in Artificial Intelligence-Generated Images: A Content Analysis.Journal of palliative medicine · 2026Article
- Bias, representation, and clinical fidelity in AI-generated images for medical education: a systematic literature review.NPJ digital medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeCancer 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.
methodsTwo 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.
resultsCancer 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).
conclusionsAI-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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.