Credibility of AI generated and human video doctors and the relationship to social media use

Bibliographic Data

ID22085657
AuthorsTao Liu (0000-0001-5480-2568, Central South University), Peijia Wang (Hunan University, corresponding author), Deyin Pan (Huazhong University of Science and Technology), Ruixin Liu (0000-0002-5526-6717, Beijing University of Chinese Medicine)
Year2025
Volume13
Pages1559378-1559378
Publication date2025-07-09
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2025.1559378
PMID40703166
OpenAlexW4412140295
LanguageEN
Citations received1
References cited34

Objective It’s unclear if the age stereotype of young doctors also applies to artificial intelligence (AI) doctors. Although research shows social media can reduce discrimination, age stereotypes are still underexplored. This study is aimed to determine the relationship between social media use and age stereotypes among doctors in online health videos narrated by AI/human doctors. Methods This is a cross-sectional study, divided into two phases and conducted from May 25 to June 19, 2024. Self-reported questionnaire was developed and collected by face-to-face interview. All individuals who are 18 years old or above with adequate reading comprehension skills are eligible. The credibility of doctors among participants in online health videos in AI and human conditions and their relationship with the intensity of social media use were investigated. Univariable and multivariable generalized linear models were used to explore the relationship between social media use and age stereotypes. Results We obtained 294 and 300 valid questionnaires in phase I and phase II, respectively. In both AI and human conditions, there is a preference for health education conducted by older doctors. Older doctors were rated the most credible (median score 14·00, IQR [12·00, 15·00] in the condition of AI, median score 14·00, IQR [12·00, 15·00] in the condition of Human). Both univariable and multivariable generalized linear models revealed a significant negative association between social media use and age stereotypes, particularly between older and younger doctors ( β = −0·34, p < 0.001). In the condition of AI. In the condition of human, the intensity of social media use is not related to participants’ age stereotypes. Conclusion The image of AI doctors can help patients avoid being influenced by age stereotypes, enabling them to evaluate doctors’ medical expertise more objectively

Credibility · Data science · Internet privacy · Political science · Social media · World Wide Web · Aging and Gerontology Research · Artificial Intelligence in Healthcare and Education · Computer Science · Medicine · Technology Use by Older Adults

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