人工智能/机器学习中的伦理和偏见考虑
Matthew G Hanna1, Liron Pantanowitz1, Brian Jackson2
1Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania; Computational Pathology and AI Center of Excellence (CPACE), University of Pittsburgh, Pittsburgh, Pennsylvania.
概括
人工智能 (AI) 和机器学习 (ML) 提供了医学进步,但也带来了伦理风险. 仔细评估AI-ML模型对于确保公平性和防止医疗保健应用中的偏见至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 病理学 病理学 病理学
背景情况:
- 人工智能 (AI) 和机器学习 (ML) 越来越多地融入医疗实践.
- 这些技术在像图像识别和预测分析等领域显示出显著的能力.
- 然而,它们的部署引发了有关潜在偏见的伦理担忧.
研究的目的:
- 仔细研究AI和ML模型在病理学和医学中的伦理含义和潜在偏见.
- 突出解决AI-ML系统中的偏见的重要性,以实现公平的医疗保健.
- 提供医疗AI-ML应用中的伦理和偏见考虑的全面概述.
主要方法:
- 在医学和病理学领域内对AI-ML系统的伦理考虑和偏见的审查.
- 偏见来源的分类为数据偏见,发展偏见和交互偏见.
- 讨论导致偏见的因素,包括训练数据,算法和临床实践变化.
主要成果:
- 人工智能-ML模型虽然强大,但由于各种偏见,可以无意中产生不公平或有害的结果.
- 偏见可能源于训练数据,算法设计,特征选择和临床实践的变化.
- 在AI-ML在医学中的开发和临床部署过程中,道德问题至关重要.
结论:
- 对AI-ML系统来说,从开发到部署,一个全面的评估过程是必不可少的.
- 解决偏见至关重要,以确保AI-ML工具公平,透明,并有利于所有患者.
- 对于AI-ML在病理学和医学中的负责任整合,需要对伦理和偏见进行持续的审查.
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