人工智能和机器学习在神经退行性疾病管理中:一个21世纪的范式
Shaik Basha1, Pranavi Ks2, Ahana Chattopadhyay2
1Department of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, India.
Neurobiology of disease
|February 5, 2026
概括
人工智能 (AI) 提供了分析神经退行性疾病复杂数据的新方法. 人工智能模型在早期风险分层和疾病监测方面表现有希望,但性能取决于数据质量和验证.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算神经科学是一种神经科学.
- 人工智能在医学中的应用
背景情况:
- 神经退行性疾病由于其渐进性质和有限的治疗方法,造成了重大的临床挑战.
- 人工智能 (AI) 为复杂的生物医学数据提供了新的分析策略,补充了现有的研究和诊断方法.
研究的目的:
- 在神经退行性疾病中批判性地合成AI应用,重点研究2022-2025年的研究.
- 为了比较人工智能方法,它们的实施和验证在各种数据模式中.
主要方法:
- 对用于神经退行性疾病研究的人工智能方法的系统审查和比较分析.
- 在分子形状,神经成像,生物传感器,语音,步态和电子健康记录中对人工智能应用的评估.
- 专注于方法选择,实施和验证,而不是算法目录.
主要成果:
- 人工智能模型可以支持早期风险分层,疾病特征以及对阿尔茨海默氏症,帕金森症和其他神经退行性疾病的监测.
- 人工智能性能严重依赖于数据质量,特征表示,验证设计和模型数据对齐,而不仅仅是算法复杂性.
- 新兴的人工智能范式,如多式联通集成,在方法上有希望,但需要进一步开发以获得临床准备.
结论:
- 人工智能是神经退行性疾病研究和临床决策支持的支持和辅助分析框架.
- 方法透明度,不确定性意识评估和生物解释性对于严格的AI部署至关重要.
- 人工智能的潜力最大化,当谨慎地应用,专注于强大的方法和明确的临床背景.
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