Analysis · Technology
Who Approves of Eye-Screening AI May Matter to Its Adoption
By AWEI · AI-compiled · Published · Analysis prepared · 1 source · doi.org
Family, colleagues and workplace leaders featured prominently in willingness to try eye-screening AI. In a 2019 study by Tiantian Ye and colleagues, perceived approval from these people had a stronger direct association with intended use than perceived usefulness did. The finding concerns willingness, not completed screenings or better diagnoses.
Published in the Journal of Medical Internet Research, the study analyzed 474 eligible responses from an online survey in China. Participants received an introduction to ophthalmic AI devices, including photographs and operating procedures, then rated 32 statements on seven-point scales. Recruitment came from a survey company's database; ophthalmic medical staff were excluded.
The researchers used structural equation modeling to examine proposed relationships among beliefs, social expectations and intention to use. Their model accounted for 51.5% of the variation in stated intention within the sample. That figure describes how well the model explained survey responses, not its accuracy at predicting future adoption.
Perceived usefulness was positively associated with intention, while resistance rooted in unfamiliarity, anticipated regret and disappointing experiences with technology was negatively associated. Ease of use had no statistically significant direct association with intention, although the model supported an indirect relationship through perceived usefulness. This suggests a useful distinction between expecting something to be convenient and expecting it to help.
The survey captured one moment, so it cannot establish that changing social approval would cause adoption. The authors' cultural explanations were interpretations, not tested mechanisms. Respondents were predominantly young and highly educated, with few over 50, limiting transfer to older screening populations. Actual use, clinical performance and financial value were not measured.
For deployment teams, the bounded lesson is to investigate social expectations and reluctance alongside perceived utility. These findings identify questions for local evaluation; they do not establish that endorsement campaigns will increase uptake or that greater willingness will produce better care.
Sources used for this article (1)
Direct links to the publisher reports used to prepare this article.
- Source 1
- Psychosocial Factors Affecting Artificial Intelligence Adoption in Health Care in China: Cross-Sectional Study Tiantian Ye, Jiaolong Xue, Mingguang He …
