应用于全球儿童健康的人工智能:解决偏见和障碍
Vijaytha Muralidharan1, Joel Schamroth2, Alaa Youssef3
1Department of Dermatology, Stanford University, Stanford, California, United States of America.
PLOS digital health
|August 22, 2024
概括
儿童医疗保健中的人工智能和机器学习 (AI/ML) 需要针对儿童进行特定的调整.
科学领域:
- 儿科医疗保健技术 儿童医疗保健技术
- 人工智能在医学中的应用
- 机器学习应用 机器学习应用
背景情况:
- 当前医疗保健中的人工智能和机器学习 (AI/ML) 不足以解决儿科数据的特殊性和儿童和青少年 (CYP) 的脆弱性.
- 对CYP的AI/ML应用集中在高收入国家,在低收入和中等收入国家 (LMICs) 的进展有限.
- 在LMICs中现有的AI/ML努力在很大程度上处于概念验证阶段,这凸显了发展和实施的全球差异.
研究的目的:
- 确定全球挑战和必要的转变,以便在儿科研究和实践中有效地实施AI/ML.
- 突出了整个AI/ML生命周期中对儿童特定技术考虑的关键需求.
- 倡导公平地在儿童健康中部署AI/ML,解决治理和包容性问题.
主要方法:
- 这篇叙事综述综合了有关儿童医疗保健中AI/ML的现有文献.
- 它确定了交叉的挑战,并提出了跨多个领域进步的转变.
- 该审查强调以人为中心和跨学科的方法.
主要成果:
- 在AI/ML中,儿童特定的技术考虑在很大程度上被忽视了,影响了模型的有效性.
- 强大的治理框架对于在CYP护理中安全和负责任地部署AI/ML至关重要.
- 国际合作,能力建设和当地接地对于公平的AI/ML采用至关重要.
结论:
- 为了实现AI/ML在儿童健康方面的普遍益处,需要加强国际合作,能力建设和强有力的监督.
- AI/ML的发展必须包括CYP和护理人员的需求,特别是在LMICs,以避免加剧不平等.
- 以当地环境为基础的以人为中心,跨学科的方法对于在儿科护理中可持续和有效的AI/ML工具至关重要.
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