Recapitulation of Ageism in Artificial… | 2025

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45/100

✓ Peer-reviewed alzhub usefulness score 45 out of 100. Digital health tools

Topic: Risk, prevention and care in population studies

Representations of White-racialized older adults were 5-fold higher than those of other races in both years. The mean number of representations of Asian-racialized individuals increased from 20 to 31 (P=.004), and the mean number of other racialized representations also increased, from 6 to 14 (P=.007). Representations of people with a middle-class socioeconomic status were significantly more frequent than other statuses in 2022 and 2023 with no changes in socioeconomic status from one year to the next. Prompts were largely neutral for expression terms, while image analyses for expressions did not show significant differences in positive, neutral, or negative emotions between 2022 and 2023. Prompts used for image generation had more male-oriented terms than expected, and male representation was higher then female representation in the images, with no difference in sex representation between the 2 time points. Despite a social emphasis on positive views on aging, AI text-to-image generators persistently generated images with characteristics of digital ageism. Images predominantly featured White-racialized individuals at both time points, with no improvement in emotional representation despite using neutral text prompts. These findings highlight the persistence of ageist visual characteristics in AI-generated images over time. A limitation of this study is that it focused only on AI image generation and did not analyze other AI-generated content that may express digital ageism.

Key findings

Representations of White-racialized older adults were 5-fold higher than those of other races in both years.
The mean number of representations of Asian-racialized individuals increased from 20 to 31 (P=.004), and the mean number of other racialized representations also increased, from 6 to 14 (P=.007).
Representations of people with a middle-class socioeconomic status were significantly more frequent than other statuses in 2022 and 2023 with no changes in socioeconomic status from one year to the next.
Prompts were largely neutral for expression terms, while image analyses for expressions did not show significant differences in positive, neutral, or negative emotions between 2022 and 2023.
Prompts used for image generation had more male-oriented terms than expected, and male representation was higher then female representation in the images, with no difference in sex representation between the 2 time points.

Why it matters

This study found that AI text-to-image generators kept producing ageist images over two years, mostly of White-racialized older adults, with no improvement in emotional representation despite neutral prompts. It is about bias in generated images and has no bearing on treating, curing or preventing Alzheimer's disease.

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Study details

Study designlongitudinal study
Evidence strengthModerate

How this study scored 45/100

Published in a peer-reviewed journal, and it is a longitudinal study following the same people over time. Scored zero for sample size, effect reported, studied in people and randomised / blinded because the record does not state it.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (Journal of Medical Internet Research)
Journal's citation record	3 / 4	Journal of Medical Internet Research: journal h-index 286 (OpenAlex, 2026-10-29)
Study design	7 / 12	A longitudinal study following the same people over time
Strength of evidence	3 / 5	Rated “moderate” in our summary
Peer-review validation	10 / 10	Peer-reviewed. Published as an article in a named peer-reviewed journal.
How recent	4 / 5	2025 — within 3 year(s) of the newest study here
Sample size	0 / 4	Participant count not stated — scores zero (not stated in the record — scored 0)
Effect reported	0 / 3	No effect size reported — scores zero (not stated in the record — scored 0)
Studied in people	0 / 3	Not stated whether this was studied in people — scores zero (not stated in the record — scored 0)
Randomised / blinded	0 / 2	No randomisation or blinding stated — scores zero (not stated in the record — scored 0)
Times cited	1 / 7	Cited 4 times (OpenAlex, 2026-10-10)
Citations per year	1 / 5	1.3 citations a year over 3 year(s) since 2025
Impact for its field and year	3 / 6	Cited 4.4× the average for papers of its field and year (OpenAlex FWCI)
Cited by studies here	0 / 6	No other study in this library cites it
Still being cited	1 / 4	Cited 4 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	1 / 4	Last-listed author Donald A. Jurivich: h-index 18 (OpenAlex, whole career)
Institution's track record	0 / 3	University of North Dakota: institution h-index 305 (OpenAlex)
Multi-centre collaboration	0 / 3	OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete)
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 40803004)
Free to read	2 / 2	Free to read
Total	45 / 100	—

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Read the original

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

Cite this study APA, Vancouver, BibTeX · download .bib or .ris

Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.

APA 7th

Martens, L., Virgin, N., Hoffarth, P., Goodwill, J., Derenne, N., Van Eck, R. N., et al. (2025). Recapitulation of Ageism in Artificial Intelligence–Generated Images: Longitudinal Comparative Study. Journal of Medical Internet Research. https://doi.org/10.2196/68428

Vancouver

Martens L, Virgin N, Hoffarth P, Goodwill J, Derenne N, Van Eck RN, et al. Recapitulation of Ageism in Artificial Intelligence–Generated Images: Longitudinal Comparative Study. Journal of Medical Internet Research. 2025. doi:10.2196/68428. PMID: 40803004.

BibTeX

@article{martens2025-40803004,
  author  = {Martens, Lindsey and Virgin, Nicole and Hoffarth, Phillip and Goodwill, Jade and Derenne, Nicole and Van Eck, Richard N. and Klug, Marilyn G. and Manocha, Gunjan Dhawan and others},
  title   = {{Recapitulation of Ageism in Artificial Intelligence–Generated Images: Longitudinal Comparative Study}},
  journal = {Journal of Medical Internet Research},
  year    = {2025},
  doi     = {10.2196/68428},
  pmid    = {40803004},
  url     = {https://doi.org/10.2196/68428}
}

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