人工智能辅助的医疗记录数据挖掘用于癌症检测和查
Amalie Dahl Haue1, Jessica Xin Hjaltelin2, Peter Christoffer Holm2
1Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Copenhagen University Hospital Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.
The Lancet. Oncology
|December 5, 2024
概括
应用于电子健康记录的人工智能 (AI) 能够使用深度表型识别早期检测癌症. 在前性测试,偏见评估和全球公平癌症查数据多样性方面仍然存在挑战.
科学领域:
- 计算生物学是一种计算生物学.
- 医疗信息学医学信息学
- 在瘤学瘤学.
背景情况:
- 电子健康记录 (EHR) 提供了大量的多式联络数据,用于全人口的分析.
- 应用于电子健康记录的人工智能 (AI) 方法有助于创建数据驱动算法.
- 这些算法在改善早期癌症检测和查方法方面表现有前途.
研究的目的:
- 审查用于早期癌症检测的AI方法.
- 突出实施人工智能用于癌症查的关键挑战和考虑因素.
- 讨论癌症查获取的伦理方面和全球差异.
主要方法:
- 对用于癌症检测的EHR应用的当前AI方法的审查.
- 分析挑战,包括数据漂移,偏差评估和数据集多样性.
- 检查癌症查中的伦理考虑和全球获取不平等.
主要成果:
- 用人工智能驱动的EHR分析使得癌症检测的大规模深度表型化成为可能.
- 在开发用于早期癌症检测和查的AI算法方面取得了重大进展.
- 关键的挑战包括前性测试,偏差评估和数据异质性的基础设施.
结论:
- 人工智能在早期癌症检测和查方面具有重大潜力.
- 解决数据漂移,偏差和数据多样性对于可靠的AI实施至关重要.
- 在全球范围内,公平获得人工智能驱动的癌症查仍然是一个关键问题,特别是在高收入国家与低收入和中等收入国家进行比较时.
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