医疗成像放射学联合学习
Muhammad Habib Ur Rehman1,2, Walter Hugo Lopez Pinaya1,2, Parashkev Nachev3
1Division of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
The British journal of radiology
|November 27, 2023
概括
医疗AI中的联合学习 (FL) 提供了准确性和隐私,但在现实应用中落后. 这一综述弥合了已发表的研究与临床实践之间的差距,指导了多机构合作的未来发展.
科学领域:
- 人工智能在医学中的应用
- 机器学习用于医疗保健
- 医学成像分析 医学成像分析
背景情况:
- 联合学习 (FL) 在医疗AI中越来越多地被采用,因为它有可能提高模型的准确性,确保患者的隐私,并使各机构的通用性.
- 尽管有希望,但FL专门用于医学成像AI的研究仍在芽,理论进展和实际临床实施之间存在显著的分歧.
研究的目的:
- 审查医疗成像AI联合学习的当前研究.
- 将最先进的技术 (SOTA) 研究和最先进的实践 (SOTP) 应用区分开来.
- 确定未来的研究轨迹,将SOTA的发现转化为SOTP的临床现实.
主要方法:
- 对医学成像中联合学习的最新文献进行系统审查.
- 发表的研究 (SOTA) 与应用临床研究 (SOTP) 的比较分析.
- 确定影响SOTA到SOTP转换的关键因素.
主要成果:
- 在公布的联合学习研究与其在临床环境中的应用之间发现了显著的差距.
- 会议强调了数据处理,模型开发,系统架构,治理和人-in-loop集成方面的关键挑战.
- 建议未来的研究方向是为了促进FL从理论到实际的医学成像应用的过渡.
结论:
- 联合学习对医学成像AI有很大的前景,但实际实施需要解决已识别的研究差距.
- 将最先进的发现转化为最先进的实践需要集中精力在数据,模型,系统,治理和人类-人工智能协作上.
- 进一步的研究对于实现FL在多机构医疗AI合作和临床部署方面的全部潜力至关重要.
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