自动机器学习的临床性能:系统性审查
Arun James Thirunavukarasu1,2, Kabilan Elangovan1, Laura Gutierrez1
1Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore.
Annals of the Academy of Medicine, Singapore
|June 26, 2024
概括
自动机器学习 (autoML) 在各种应用中显示了可变但具有竞争力的临床性能. 需要进一步的研究来提高AI驱动的医疗保健进步的验证研究质量.
科学领域:
- 人工智能 (AI) 和机器学习的临床应用.
- 医疗信息学和数据科学.
- 医疗保健技术和创新.
背景情况:
- 自动机器学习 (autoML) 旨在通过减少技术障碍来使人工智能模型开发民主化.
- 这次审查系统评估了autoML的临床实用性,平台功能,证据质量和性能基准.
- 该研究探讨了人工智能在医学研究和实践中的日益集成问题.
结论:
- 自动ML平台显示出有前途的临床应用和与传统方法相比的性能.
- 提高验证研究的质量对于在医疗保健中可靠地实施自动ML至关重要.
- 未来的方向包括以数据为中心的开发和与自我改进的人工智能系统的大型语言模型的集成.
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