CADUCEO:一个平台,通过人工智能支持联邦医疗保健设施
Danilo Menegatti1, Alessandro Giuseppi1, Francesco Delli Priscoli1
1Department of Computer, Control and Management Engineering "Antonio Ruberti", Sapienza University of Rome, Via Ariosto 25, 00185 Rome, Italy.
Healthcare (Basel, Switzerland)
|August 12, 2023
概括
人工智能 (AI) 算法在医学上表现有前途,但面临着数据和可解释性挑战. 使用数据增强和联合学习的新AI框架可以克服临床应用中的这些障碍.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床决策支持系统 临床决策支持系统
背景情况:
- 数据驱动的算法在疾病预测和诊断等医疗任务中表现出色,通常与临床医生的表现相匹配.
- 人工智能的广泛临床采用受到高质量,代表性数据的需求,缺乏可解释性和监管方面的担忧所阻碍.
研究的目的:
- 在临床环境中解决当前AI算法的局限性.
- 提出基于人工智能的新功能框架,以促进更广泛的AI在医疗保健中的应用.
主要方法:
- 使用数据增强和合成数据生成技术.
- 实施联合学习,用于保护隐私的数据分析.
- 采用可解释的人工智能 (XAI) 方法来提高透明度.
主要成果:
- 拟议的框架旨在克服临床AI中的数据质量和可解释性挑战.
- 有潜力将医疗预防,诊断和预后的时间缩短高达70%.
结论:
- 新的AI框架为将AI整合到临床实践中提供了一个可行的解决方案.
- 联合学习和XAI是实现更广泛的人工智能采用和改善患者治疗结果的关键.
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