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This bias has been detected in other applications of generative AI in medicine, including in the range of skin tone present in images generated by Dall-E 3 and Midjourney [28], and in representations of sex or gender in AI-generated patient vignettes [29]. In both cases, the bias in question was mitigated by the addition of real demographic data into the relevant prompt.
A potential limitation of this study is that only a fraction of LMIC-prevalent conditions were investigated.
JMIR Form Res 2025;9:e64986
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Preventing Urgent Pediatric Readmissions: The Need for and Promise of Real-Time Monitoring
JMIR Pediatr Parent 2025;8:e60802
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For compensation, participants were either entered into a draw for 10 Aus $100 (US $65) e-gift cards (cohort 1) or received an Aus $50 (US $33) e-gift card (cohort 2).
Three unique identifiers were randomly assigned to each participant: one upon receipt of the study invitation; one during screening; and one upon confirmation of eligibility. These identifiers enabled linkage between baseline and digital phenotyping data for participants from AGDS.
JMIR Form Res 2025;9:e71377
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