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http://dx.doi.org/10.1016/j.jcjo.2023.09.006 | DOI Listing |
Sci Adv
September 2025
Questrom School of Business, Boston University, Boston, MA 02215, USA.
Artists are rapidly integrating generative text-to-image models into their workflows, yet how this affects creative discovery remains unclear. Leveraging large-scale data from an online art platform, we compare artificial intelligence (AI)-assisted creators to matched nonadopters to assess novel idea contributions. Initially, a concentrated subset of AI-assisted creators contributes more novel artifacts in absolute terms through increased output-the productivity effect-although the average rate of contributing novel artifacts decreases because of a dilution effect.
View Article and Find Full Text PDFSci Rep
August 2025
Interchange Forum for Reflecting on Intelligent Systems, University of Stuttgart, Stuttgart, Germany.
Text-to-image models are increasingly popular and impactful, yet concerns regarding their safety and fairness remain. This study investigates the ability of ten popular Stable Diffusion models to generate harmful images, including sexual, violent, and personally sensitive material. We demonstrate that these models respond to harmful prompts by generating inappropriate content, which frequently displays troubling biases, such as the disproportionate portrayal of Black individuals in violent contexts.
View Article and Find Full Text PDFJ Med Internet Res
August 2025
Department of Geriatrics, School of Medicine and Health Sciences, University of North Dakota, 1301 N Columbia Rd, Grand Forks, ND, 58202, United States, 1 7017774455.
Background: Positive images of aging in traditional media promote better health outcomes in older adults, including increased life expectancy. Images produced by generative artifical intelligence (AI) technologies may reflect and amplify societal age-related biases, a phenomenon known as digital ageism. This study addresses a gap in research on the perpetuation of digital ageism in AI-generated images over time.
View Article and Find Full Text PDFHealth Commun
August 2025
Health Communication and Informatics Research Branch, National Cancer Institute.
This study sought to characterize images of cancer patients generated by Artificial Intelligence (AI) text-to-image tools, and assess whether images differed by cancer type or AI tool, to elucidate the potential implications of using AI-generated images in health communication. Two generative AI-based tools, and , were prompted to produce images of a "cancer patient," "breast cancer patient," "lung cancer patient," and "prostate cancer patient". Images ( = 320) were coded for perceived demographics, illness features, affect, cancer symbols, setting, and photorealism.
View Article and Find Full Text PDFNPJ Digit Med
July 2025
cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, University Hospital, Ludwig-Maximilians-Universität, Munich, Germany.
The wide usage of artificial intelligence (AI) text-to-image generators raises concerns about the role of AI in amplifying misconceptions in healthcare. This study therefore evaluated the demographic accuracy and potential biases in the depiction of patients by four commonly used text-to-image generators. A total of 9060 images of patients with 29 different diseases was generated using Adobe Firefly, Bing Image Generator, Meta Imagine, and Midjourney.
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