对跨大西洋协作健康数据使用和人工智能开发的有趣愿景.
1Precision Health Signature Area, College of Health Sciences, College of Natural and Applied Sciences all at University of Alberta, Edmonton, Alberta, Canada. baumgart@ualberta.ca.
精确的健康需要集成系统和人工智能 (AI) 来获得更好的患者结果. 开发可靠的人工智能需要协作,多样化的数据集,以及向信任竞争前数据共享的文化转变.
科学领域:
- 数字健康数字健康
- 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:
- 传统的医疗保健正在与大量数据作斗争,阻碍了治疗成功和创新.
- 精确健康,提供量身定制的治疗,依赖于集成学习健康系统.
- 人工智能 (AI) 在风险分层,诊断和减少健康差异方面表现有前途.
研究的目的:
- 突出目前医疗数据处理的局限性.
- 通过学习健康系统和人工智能,倡导精确健康.
- 强调对人工智能开发的协作,代表性数据的需求.
主要方法:
- 讨论了可靠的人工智能模型的要求,包括大型的人口代表性数据集.
- 强调需要多学科,跨国团队来缓解偏见.
- 引用了数据为健康 (#DFH23) 会议和哈佛研讨会作为利益相关方参与的例子.
主要成果:
- 人工智能模型需要多样化,大规模的数据和协作努力,以避免偏见.
- 目前的数据基础设施不足以在国家或机构层面开发和验证人工智能.
- 在竞争前的数据共享中,合作和信任的新维度至关重要.
结论:
- 欧洲健康数据空间 (EHDS) 和七国集团广岛人工智能进程为数据协作提供了框架.
- 向信任和竞争前数据共享的文化转变对于推进医学人工智能至关重要.
- 呼吁全面支持医疗,研究和创新的数字化转型,包括人工智能.
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