用数据打:iToBoS项目中的健康数据的风险,益处,紧张和技术
Niamh Aspell1, Abigail Goldsteen2, Robin Renwick1
1Innovation & Research, Trilateral Research Ltd., Waterford, Ireland.
该iToBoS项目开发了一个用于早期皮肤黑色素瘤检测的AI平台,面临数据隐私和利益相关者沟通方面的挑战. 与患者网络的合作提供了一个解决方案,尽管可扩展性仍然是人工智能驱动的医疗保健的一个问题.
科学领域:
- 医疗人工智能 医疗人工智能
- 皮肤病学 皮肤病学
- 生物信息学是一种生物信息学.
背景情况:
- 欧洲iToBoS项目旨在创建一个用于早期皮肤黑色素瘤检测的AI诊断平台.
- 该项目解决了人工智能驱动医疗保健中的数据隐私,知情同意和利益相关者沟通方面的复杂性.
研究的目的:
- 概述iToBoS项目,其数据处理,影响评估和AI隐私风险缓解策略.
- 讨论与小型研究队伍,知情同意,开放数据和算法透明度相关的挑战.
- 在人工智能增强的健康领域探索创新和隐私之间的紧张关系.
主要方法:
- 开发一种针对皮肤黑色素瘤的AI诊断平台.
- 实施人工智能隐私风险减轻技术.
- 与黑色素瘤患者网络合作进行风险效益评估.
主要成果:
- 在小型队列中识别了数据区分和匿名化方面的挑战.
- 探讨了获得复杂人工智能技术知情同意的困难.
- 强调需要加强对开放研究中的多模式和基因组数据的隐私措施.
结论:
- 像患者网络协作这样的项目级解决方案是有价值的,但也引发了可扩展性的问题.
- 在iToBoS项目中发现的紧张局势反映了在医疗保健中采用人工智能的更广泛挑战.
- 追求创新,开放数据和多模式数据集需要强有力的隐私保护措施.
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