缺乏数据访问,但不是可用性,阻碍了安大略省对高风险条件的AI培训
Fahreen Walimohamed1, Karim Keshavjee1
1Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
Studies in health technology and informatics
|February 19, 2024
概括
人工智能 (AI) 可以早期预测疾病,但需要可访问的医疗保健数据进行培训. 改进的数据基础设施对于积极的医疗保健和在疾病预防中部署人工智能至关重要.
科学领域:
- 医疗保健信息学 医疗保健信息学
- 人工智能在医学中的应用
- 预测分析是一种预测分析.
背景情况:
- 积极的医疗保健系统依赖于先进的疾病预测.
- 人工智能 (AI) 为早期疾病发病预测提供了巨大的潜力.
- 人工智能模型培训需要大型,可访问的数据集.
研究的目的:
- 突出了对可访问的医疗保健数据对于AI驱动的疾病预测的关键需求.
- 解决人工智能模型培训数据可用性和可访问性之间的差距.
- 强调AI在预防高成本,可预测疾病方面的潜力.
主要方法:
- 对人工智能培训的医疗保健数据可访问性的分析.
- 识别人工智能可预测和治疗可预防的高成本疾病.
- 对人工智能部署当前基础设施限制的评估.
主要成果:
- 医疗保健数据可用,但对AI来说不容易访问或实时访问.
- 五种高成本疾病被认为是人工智能可以预测和预防的.
- 为了实现可扩展的AI实施,需要大量的基础设施投资.
结论:
- 政府和利益相关方需要采取紧急行动,以改善数据的可访问性.
- 对数据基础设施的投资对于培训和大规模部署人工智能至关重要.
- 增强数据可访问性将促进人工智能驱动的积极主动医疗保健和疾病预防.
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