{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T17:49:31Z","timestamp":1792000171007,"version":"4.4.0"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T00:00:00Z","timestamp":1761523200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Health, Welfare and Sports","award":["90001586"],"award-info":[{"award-number":["90001586"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,2,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This content analysis study investigates potential biases in image generation by 2 artificial intelligence (AI) tools, DALL-E 3 and Midjourney, in portraying older adults and individuals living with dementia. Despite widespread use of generative AI in various sectors, there is limited research on how these models might perpetuate stereotypes and stigmatization through their images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>1056 images were generated using specified prompts categorized into 3 groups: general older adults, dementia-related, and control. Each prompt began with \u201cphotorealistic portrait\u201d followed by specific scene descriptions. Four researchers conducted content analysis on each generated image, focusing on factors, such as portrait style, setting, posture, apparent sex of subjects, and emotional affect. The analysis was executed with blinding and randomization protocols to ensure unbiased assessment. Chi-square tests examined the relationship between prompt categories and variables.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Results revealed significant disparities in depictions of older adults and those with dementia compared with control images. Both models more often portrayed subjects in response to dementia-related prompts with negative affect, in less favorable emotional states. However, DALL-E 3 also generated more personas displaying positive affect in response to these prompts. Variations in depiction styles between the 2 AI models were noted, with DALL-E 3 showing a broader diversity of outputs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion and Conclusions<\/jats:title>\n                    <jats:p>The findings highlight AI's potential to reinforce stigmatizing stereotypes through biased image generation. Recommendations include selecting prompts carefully to avoid negative depictions and advocating for greater AI explainability and inclusivity by design. Future research should explore other AI models, other forms of bias, and strategies to mitigate biases.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamia\/ocaf173","type":"journal-article","created":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T11:57:57Z","timestamp":1759406277000},"page":"442-450","source":"Crossref","is-referenced-by-count":1,"title":["Biases in an artificial intelligence image-generator\u2019s depictions of healthy aging and Alzheimer\u2019s"],"prefix":"10.1093","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2830-1207","authenticated-orcid":false,"given":"Channah","family":"Osinga","sequence":"first","affiliation":[{"name":"Department of Clinical Neuropsychology, VU University Amsterdam , Amsterdam, 1081 BT,","place":["the Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Natcha","family":"Jintaganon","sequence":"additional","affiliation":[{"name":"Department of Medical Informatics, Amsterdam UMC , Amsterdam, 1100 DD,","place":["the Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dirk","family":"Steijger","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Neuropsychology, Faculty of Health, Medicine and Life Sciences, Alzheimer Centrum Limburg, Mental Health and Neuroscience Research Institute, Maastricht University , Maastricht, 6229 ET,","place":["the Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marjolein","family":"De Vugt","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Neuropsychology, Faculty of Health, Medicine and Life Sciences, Alzheimer Centrum Limburg, Mental Health and Neuroscience Research Institute, Maastricht University , Maastricht, 6229 ET,","place":["the Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Neal","sequence":"additional","affiliation":[{"name":"Department of Medical Informatics, Amsterdam UMC , Amsterdam, 1100 DD,","place":["the Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,10,27]]},"reference":[{"key":"2026012716172241100_ocaf173-B1","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1001\/jama.2023.25054","article-title":"Will generative artificial intelligence deliver on its promise in health care?","volume":"331","author":"Wachter","year":"2024","journal-title":"JAMA"},{"key":"2026012716172241100_ocaf173-B2","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1080\/13600834.2024.2352694","article-title":"Legal implications of using generative AI in the media","volume":"33","author":"Bayer","year":"2024","journal-title":"Inf Commun Technol Law"},{"key":"2026012716172241100_ocaf173-B3","first-page":"e84507","article-title":"A cross-sectional comparison of patient information guides generated by ChatGPT versus Google Gemini for Alzheimer\u2019s disease, Parkinsonism, and Migraine","volume":"17","author":"Chaulagain","year":"2025","journal-title":"Cureus"},{"key":"2026012716172241100_ocaf173-B4","doi-asserted-by":"publisher","first-page":"101","DOI":"10.11425\/sst.13.101","article-title":"Exploring AI-generated images as memory aids in elderly care A preliminary study","volume":"13","author":"Shimizu","year":"2024","journal-title":"Stud Sci Technol"},{"key":"2026012716172241100_ocaf173-B5","volume-title":"Global Status Report on the Public Health Response to Dementia","author":"World Health Organization","year":"2021"},{"key":"2026012716172241100_ocaf173-B6","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1176\/appi.focus.20210010","article-title":"Check your ageism at the door: implicit bias in the care of older patients","volume":"19","author":"Stubbe","year":"2021","journal-title":"FOC"},{"key":"2026012716172241100_ocaf173-B7","doi-asserted-by":"publisher","DOI":"10.17226\/26173","volume-title":"Understanding the Aging Workforce: Defining a Research Agenda","author":"National Academies of Sciences Engineering, and Medicine; 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