人工智能/机器学习用于和的诊断
Kenneth Han1, Chris Liu2, Daniel Friedman1
1Departments of Neurology, NYU Grossman School of Medicine, New York, NY, United States.
Epilepsy & behavior : E&B
|April 18, 2024
概括
机器学习和人工智能 (AI) 通过分析EEG和神经成像等复杂数据来改善的诊断. 克服数据挑战是实现AI的关键.
科学领域:
- 神经学 神经学
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
背景情况:
- 的诊断是复杂的,往往导致延迟或误诊.
- 机器学习 (ML) 和人工智能 (AI) 为诊断不确定性提供了潜在的解决方案.
- 现有的诊断方法难以应对发作表现的变化.
研究的目的:
- 审查AI/ML工具在诊断中的开发和应用.
- 突出AI在解释各种诊断数据中的实用性,包括EEG和神经成像.
- 讨论AI整合到临床实践中的障碍和未来方向.
主要方法:
- 对近期关于AI/ML在诊断中的应用的出版物的审查.
- 分析AI/ML工具的开发,测试,概括性和可解释性.
- 检查人工智能实施的数据要求和挑战.
主要成果:
- 人工智能/ML,特别是深度神经网络,越来越多地用于解释EEG,神经成像和其他患者数据.
- 最近的研究表明,人工智能有潜力帮助临床医生诊断.
- 数据集的异质性和可用性是影响人工智能研究质量和概括性的当前限制.
结论:
- 人工智能和机器学习对提高诊断的准确性和效率具有重大前景.
- 解决数据的局限性和提高可解释性对于临床AI集成至关重要.
- 数据集,处理速度和标准化的进步将推动人工智能在治疗中的应用.
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