人工智能技术在儿童早期健康发展评估中的应用综述OA

Early childhood health development assessment plays a critical role in shaping lifelong capabilities and achieving health equity,but its multidimensional and dynamic nature demands assessment tools that are both precise and adaptable.This systematic review compares the effectiveness of traditional assessment tools and artificial intelligence(AI)—assisted technologies.Traditional tools establish standardized developmental benchmarks but demonstrate limitations including examiner dependency,insufficient cultural adaptability,and inadequate dynamic tracking capabilities.AI—assisted technologies improve assessment objectivity in autism spectrum screening and language disorder prediction through multimodal data fusion and algorithmic optimization,while confronting clinical translation barriers regarding data heterogeneity,model interpretability,and ethical concerns.The development of culturally-adaptive multimodal AI models to establish integrated dynamic monitoring-intervention systems represents a key direction for personalized early childhood health assessment.