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机器学习在自闭症谱系障碍评估和管理中的作用
Aoife Reilly1, Nicola Walsh2, Daniel O'Reilly3,4
1Department of Neonatology, Rotunda Hospital, Dublin, Ireland. aoifesreilly@outlook.com.
Pediatric research
|November 14, 2025
概括
机器学习和人工智能为发现,分层和治疗自闭症谱系障碍 (ASD) 提供了有希望的新方法. 虽然人工智能的进步显示了变革的潜力,但数据问题和临床信任等挑战仍然存在.
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:
- 自闭症谱系障碍 (ASD) 的特点是异质性和复杂的病因,这给诊断和治疗带来了重大挑战.
- 机器学习 (ML) 和人工智能 (AI) 的最新进展为ASD提供了新的方法.
研究的目的:
- 审查目前在自闭症谱系障碍中的ML和AI研究的现状.
- 探索人工智能在早期查,表型分层,生物标志物发现,神经成像,个性化疗法和ASD机器人的应用.
主要方法:
- 这篇叙述性综述综合了关于在ASD中ML和AI应用的当前研究.
- 分析的关键领域包括查,诊断,分层,神经成像和治疗干预.
主要成果:
- 机器学习和人工智能在改善自闭症患者的结果方面显示出具有变革潜力.
- 新兴趋势包括多式联络人工智能,数字表型化和人工智能驱动的精密医学.
- 在数据集异质性,验证和临床信任方面存在重大差距.
结论:
- 人工智能和机器学习为推进ASD研究和临床实践提供了强大的工具.
- 解决目前的研究缺口对于实现AI在ASD中的全部潜力至关重要.
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