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机器学习在自闭症谱系障碍中的演变作用:当前证据和未来方向
Khaled Saad1, Soha A Hussain2, Ahmad Roshdy Ahmad3
1Department of Pediatrics, Assiut University, Assiut, Egypt. ksaad8@yahoo.com.
Pediatric research
|December 16, 2025
概括
机器学习 (ML) 增强了人工智能 (AI) 的自闭症谱系障碍 (ASD) 查和诊断. 机器学习工具显示出潜在的支持,而不是取代ASD评估中的临床专业知识.
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:
- 人工智能 (AI) 和机器学习 (ML) 越来越多地被用于医疗保健.
- 自闭症谱系障碍 (ASD) 的诊断和管理带来了复杂的挑战.
- 在ASD评估中,ML提供了客观数据分析的潜力.
研究的目的:
- 审查ML在推进AI驱动的ASD策略中的作用.
- 评估ML在ASD的查,诊断,亚型和治疗干预中的应用.
- 讨论ML在ASD中增强临床判断的潜力.
主要方法:
- 对ASD中ML应用的当前文献的综述.
- 对用于ASD查和诊断的AI驱动工具的分析.
- 检查ML在ASD亚型和治疗策略中的作用.
主要成果:
- ML是人工智能驱动的ASD策略中的一个重要因素,影响查,诊断和亚型.
- 机器学习工具显示了在ASD中补充,而不是取代专家临床评估的潜力.
- 目前的ML应用显示出提高ASD评估效率和客观性的承诺.
结论:
- 机器学习的进步对于ASD的AI至关重要,有助于查和诊断.
- ML的作用是支持,而不是取代ASD评估中的临床专业知识.
- 未来的研究需要标准化数据,可解释的模型,并为现实世界ASD应用程序进行多中心验证.
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