Digital Ethics in Care: Equity-Centered AI Frameworks in Pediatric and Family Nursing
محل انتشار: سی و هفتمین کنگره بیماری های کودکان
سال انتشار: 1404
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 21
متن کامل این مقاله منتشر نشده است و فقط به صورت چکیده یا چکیده مبسوط در پایگاه موجود می باشد.
توضیح: معمولا کلیه مقالاتی که کمتر از ۵ صفحه باشند در پایگاه سیویلیکا اصل مقاله (فول تکست) محسوب نمی شوند و فقط کاربران عضو بدون کسر اعتبار می توانند فایل آنها را دریافت نمایند.
- صدور گواهی نمایه سازی
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
PEDIATRICS37_100
تاریخ نمایه سازی: 14 شهریور 1405
چکیده مقاله:
Background Artificial intelligence (AI) is transforming pediatric and family nursing through enhanced diagnostics, personalized care plans, and family support. Yet, ethical challenges including algorithmic bias, data privacy, and unequal access-pose risks for vulnerable children, particularly those from marginalized communities. Equity-focused frameworks seek to ensure justice, fairness, and inclusivity in AI adoption, preventing the amplification of health disparities in hospitals and community care. Objective To systematically review equity-oriented ethical frameworks for AI in pediatric and family nursing, outlining their development, implementation, and effects on justice, and identifying gaps for future practice. Methods Following PRISMA guidelines, a systematic search (۲۰۲۰-۲۰۲۵) was conducted in PubMed, CINAHL, Scopus, Web of Science, and PsycINFO. Eligible publications included empirical studies, reviews, and policy analyses addressing bias mitigation, privacy, equity, and justice in AI applications for pediatric/family nursing. Results Twenty-two studies were included. Core principles emphasized transparency, accountability, and inclusivity. Strategies such as bias audits and participatory design improved equity scores by ۲۵-۴۰% in AI-driven diagnostics. While AI enhanced early disease prediction (۸۵-۹۵% accuracy for neonatal sepsis), biases disproportionately affected racial/ethnic minorities. Nurse education programs boosted ethical competencies (effect size: d= ۰.۶۵). Challenges included privacy breaches (۱۵-۳۰% prevalence) and limited family involvement in AI design. Integrated frameworks facilitated fair resource allocation and improved family-centered outcomes (RR = ۱.۴۵, ۹۵% CI ۱.۲۰-۱.۷۵). Conclusion Equity-centered AI ethics frameworks are crucial for fair and safe integration in pediatric and family nursing. Recommendations include nurse-led ethics training, systematic bias mitigation, and inclusive governance policies to align AI adoption with health equity goals.
کلیدواژه ها:
نویسندگان
سیما پورتیمور
Patient Safety Research Center, Clinical Research Institute, Urmia University of Medical Sciences, Urmia, Iran
مهدی محمودزاده
Department of Pediatric Nursing, Faculty of Nursing and Midwifery, Khoy
ثنا خلیل زاده ضیاء
Department of Pediatric Nursing, Faculty of Nursing and Midwifery, Khoy