大数据和人工智能促进健康中的性别平等:偏见是一个巨大的挑战
Anagha Joshi1,2,3
1Computational Biology Unit, Department of Clinical Science, University of Bergen, Bergen, Norway.
Frontiers in big data
|October 31, 2024
概括
人工智能 (AI) 和机器学习 (ML) 可以促进妇女健康,但伦理问题和数据偏见可能会加剧健康不平等. 谨慎的实施对于公平的护理至关重要.
科学领域:
- 妇女健康 妇女健康
- 人工智能的人工智能
- 机器学习 机器学习
- 健康 公平 卫生 公平
背景情况:
- 人工智能和机器学习为妇女健康提供了变革性的潜力,改善了诊断,治疗个性化和预防护理的预测建模.
- 应用涵盖了需要可访问,负担得起和基于证据的医疗保健解决方案的领域.
- 尽管有潜力,但广泛的临床采用面临着重大障碍.
研究的目的:
- 详细阐述大数据和女性健康中的ML的承诺.
- 确定阻碍临床环境中采用ML的挑战.
- 检查ML在医疗保健中加强或减轻现有的性别和性别偏见的潜力.
主要方法:
- 视角的文章讨论了人工智能和机器学习在妇女健康中的当前状态和未来方向.
- 对道德考虑,数据挑战和潜在偏见的分析.
- 对影响医疗保健平等的社会文化因素的审查.
主要成果:
- 通过提高准确性和个性化护理,ML可以提高妇女的健康.
- 关键障碍包括道德问题,数据隐私,算法偏见和专业培训差距.
- 在ML中未经纠正的偏见可以使健康结果中的性别和性别差异延续或加剧.
结论:
- 人工智能和机器学习在促进妇女健康方面具有重大前景.
- 解决道德问题,数据质量和算法偏差对于公平实施至关重要.
- 在不仔细考虑社会文化背景的情况下,盲目整合ML工具可能会加剧现有的健康不平等.
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