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医学上的生成对抗网络:人工智能新兴创新的重要考虑因素

Phani Srivatsav Paladugu^1,2, Joshua Ong³, Nicolas Nelson²

¹Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

Annals of biomedical engineering

|July 24, 2023

概括

人工智能 (AI) 和机器学习 (ML) 提供了医学进步,但道德考虑需要监管. 生成对抗网络 (GAN) 可以通过创建合成数据来改善AI医学成像,尽管安全的临床整合仍然存在挑战.

科学领域:

医学技术医学技术医学技术
人工智能的人工智能
机器学习机器学习

背景情况:

人工智能和机器学习正在改变医学,带来新的生物伦理挑战.
快速的技术扩张需要监管框架,以确保安全和公平的部署.
生成对抗网络 (GAN) 是先进的ML技术,在医学成像方面具有显著的潜力.

研究的目的:

探索与将生成对抗网络 (GAN) 集成到医疗AI中相关的生物伦理,法律和技术挑战.
突出GAN在克服AI驱动医学成像数据限制方面的潜力.
倡导对GANs安全临床采用进行监管考虑.

主要方法:

对医学成像中的AI,ML和GAN当前文献的综述.
对人工智能医疗部署的伦理,法律和技术挑战的分析.
讨论GAN在为人工智能培训生成合成医疗图像的能力.

主要成果:

GAN可以产生合成医疗图像,解决人工智能开发中的数据集大小和类型限制.
改进的AI医学成像可以提高诊断准确性和治疗规划.
整合GAN需要仔细考虑法律,道德和技术方面.

结论:

GANs为人工智能医学成像开发当前的局限性提供了一个有希望的解决方案.
框架法规对于GAN在医疗保健中的负责任和安全整合至关重要.
应对已识别的挑战将促进人工智能在医学中的变革潜力.

关键词:

在这里,我们可以看到AIAIAI.人工智能的人工智能是人工智能.生物伦理学生物伦理学临床医学临床医学一般的反向网络一般的反向网络医疗保健医疗保健医疗保健医疗保健机器学习机器学习医学教育医学教育医学成像医学成像

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