在ADHD评估中的人工智能:从早期查到精确的差异诊断,全面审查研究进展
Cuijie Zhao1,2, Yan Xu1,2, Ruixing Li1,2
1Pediatrics Hospital, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, Henan, China.
Frontiers in artificial intelligence
|September 18, 2025
概括
通过分析复杂的数据,人工智能 (AI) 提供了对注意力缺陷多动症 (ADHD) 的客观评估. 需要进一步的研究来克服精确,个性化的ADHD诊断和管理的挑战.
科学领域:
- 神经科学是一个神经科学.
- 计算机科学 计算机科学
- 医疗信息学 医疗信息学
背景情况:
- 传统的注意力缺陷多动症 (ADHD) 诊断依赖于主观方法,导致诊断挑战.
- 症状重叠,异质性和误诊风险是当前ADHD评估中的重要问题.
- 人工智能 (AI) 通过多式联络数据分析为客观ADHD评估提供了机会.
研究的目的:
- 审查人工智能在客观ADHD评估中的当前应用.
- 探索AI在早期查,风险预测和ADHD诊断方面的潜力.
- 确定人工智能在ADHD诊断和管理中的挑战和未来方向.
主要方法:
- 在ADHD评估中对AI应用的审查,包括机器学习 (ML) 和深度学习 (DL).
- 分析人工智能在处理多模式数据中的使用:行为,神经生理学,神经成像和遗传.
- 检查AI在查,风险预测,诊断,分类,差异诊断,症状量化和亚型识别方面的作用.
主要成果:
- 人工智能模型显示出用于提取ADHD客观生物标志物的巨大潜力.
- 人工智能可以提高ADHD评估的效率,并有助于精确的差异诊断.
- 人工智能有助于对症状进行量化,并识别异构的ADHD亚型.
结论:
- 人工智能为客观,精确和个性化的ADHD评估和管理提供了有希望的途径.
- 关键的挑战包括数据标准化,模型概括,可解释性,偏见和临床验证.
- 未来的研究应该专注于大规模数据,强大的AI模型和严格的临床验证,以便在ADHD护理中负责任地实施AI.
关键词:
人工智能 (AI) 是一种人工智能.注意缺陷多动障碍 (ADHD) 是一种注意缺陷多动障碍.生物标志物 生物标志物差异诊断是一种差异诊断.机器学习 (ML) 是指机器学习.神经成像是一种神经成像.客观评价 客观评价 客观评价更多相关视频
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