人工智能技术在特定性学习障碍诊疗中的应用

陈津津; 刘钟泠

doi:10.11852/zgetbjzz2026-0422

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中国儿童保健杂志 ›› 2026, Vol. 34 ›› Issue (8) : 831-835. DOI: 10.11852/zgetbjzz2026-0422

专家笔谈

人工智能技术在特定性学习障碍诊疗中的应用

陈津津, 刘钟泠

作者信息 +

Application of artificial intelligence in the diagnosis and treatment of specific learning disorder

CHEN Jinjin, LIU Zhongling

Author information +

文章历史 +

摘要

特定性学习障碍(SLD)是学龄儿童中患病率最高的神经发育性障碍之一,其传统诊断方法存在耗时长、成本高、主观性强等局限。人工智能(AI)技术为SLD的早期识别、精准诊断与个性化干预提供了新途径。本文概述了AI在SLD诊疗中的主要应用:在辅助评估方面,结合眼动追踪技术可实现高生态效度的阅读障碍筛查,结合手写分析可量化静态字形特征与动态书写过程,结合语音声学特征分析可评估阅读流利性,结合神经影像学技术可揭示SLD的神经生物学特征;在干预方面,构建自适应智能辅导系统可实现个性化干预,利用大语言模型可为社会适应提供辅助支持。但目前仍面临数据集规模偏小、算法可解释性不足、伦理隐私风险及临床验证不充分等挑战。未来需构建大规模多中心中文数据集、发展低成本筛查工具、加强循证评价并完善监管框架,推动AI成为SLD诊疗体系变革的核心动力。

Abstract

Specific learning disorder (SLD) is one of the most prevalent neurodevelopmental disorders in school-aged children. However, conventional diagnostic workflows are frequently constrained by prolonged assessment cycles, high costs, and inherent subjectivity. Artificial intelligence (AI) has provided novel paradigms for the early identification, precision diagnosis, and personalized intervention of SLD. This review outlines the main applications of AI in the diagnosis and treatment of SLD. Regarding assisted assessment, the integration of eye-tracking technology facilitates dyslexia screening with enhanced ecological validity, while computerized handwriting analysis quantifies both static orthographic features and dynamic kinematic processes. Furthermore, acoustic analysis of speech patterns enables the objective evaluation of reading fluency, and advanced neuroimaging techniques can elucidate the underlying neurobiological characteristics of SLD. In terms of intervention, adaptive intelligent tutoring systems provide personalized intervention, and large language models offer promising auxiliary tools for social adaptation. Despite these advancements, significant challenges persist, including limited dataset scales, insufficient algorithmic interpretability, ethical concerns regarding data privacy, and inadequate clinical validation. Future efforts should prioritize the construction of large-scale, multi-center Chinese-specific datasets, the development of cost-effective screening tools, and the establishment of robust evidence-based evaluation and regulatory frameworks to promote AI as the core driving force for the transformation of the SLD diagnosis and treatment system.

导出引用

陈津津, 刘钟泠. 人工智能技术在特定性学习障碍诊疗中的应用[J]. 中国儿童保健杂志. 2026, 34(8): 831-835 https://doi.org/10.11852/zgetbjzz2026-0422

CHEN Jinjin, LIU Zhongling. Application of artificial intelligence in the diagnosis and treatment of specific learning disorder[J]. Chinese Journal of Child Health Care. 2026, 34(8): 831-835 https://doi.org/10.11852/zgetbjzz2026-0422

中图分类号： R179

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