Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA.
Ann Biomed Eng. 2023 Oct;51(10):2130-2142. doi: 10.1007/s10439-023-03304-z. Epub 2023 Jul 24.
The advent of artificial intelligence (AI) and machine learning (ML) has revolutionized the field of medicine. Although highly effective, the rapid expansion of this technology has created some anticipated and unanticipated bioethical considerations. With these powerful applications, there is a necessity for framework regulations to ensure equitable and safe deployment of technology. Generative Adversarial Networks (GANs) are emerging ML techniques that have immense applications in medical imaging due to their ability to produce synthetic medical images and aid in medical AI training. Producing accurate synthetic images with GANs can address current limitations in AI development for medical imaging and overcome current dataset type and size constraints. Offsetting these constraints can dramatically improve the development and implementation of AI medical imaging and restructure the practice of medicine. As observed with its other AI predecessors, considerations must be taken into place to help regulate its development for clinical use. In this paper, we discuss the legal, ethical, and technical challenges for future safe integration of this technology in the healthcare sector.
人工智能（AI）和机器学习（ML）的出现彻底改变了医学领域。尽管这项技术非常有效，但它的快速发展也带来了一些预期和意外的生物伦理问题。由于这些强大的应用，有必要制定框架法规，以确保技术的公平和安全部署。生成式对抗网络（GAN）是一种新兴的机器学习技术，由于其能够生成合成医学图像并辅助医学 AI 训练，因此在医学成像中有广泛的应用。GAN 可以生成准确的合成图像，这可以解决当前 AI 开发在医学成像方面的局限性，并克服当前数据集类型和大小的限制。克服这些限制可以极大地促进 AI 医学成像的发展和实施，并重构医学实践。与其他 AI 技术一样，必须考虑到一些因素，以帮助规范其在临床应用中的发展。本文讨论了在医疗保健领域中未来安全集成这项技术的法律、伦理和技术挑战。
