Department of Molecular Medicine, Scripps Research, La Jolla, CA, USA.
Nat Med. 2019 Jan;25(1):44-56. doi: 10.1038/s41591-018-0300-7. Epub 2019 Jan 7.
The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient-doctor relationship or facilitate its erosion remains to be seen.
人工智能的使用,特别是深度学习的应用,得益于标记大数据的使用,以及所有领域中显著增强的计算能力和云存储。在医学领域,这开始在三个层面上产生影响:对临床医生来说,主要是通过快速、准确的图像解释;对卫生系统来说,通过改善工作流程和减少医疗错误的潜力;对患者来说,通过使他们能够处理自己的数据来促进健康。本文将讨论这些应用的当前限制,包括偏见、隐私和安全以及缺乏透明度,以及未来的发展方向。随着时间的推移,准确性、生产力和工作流程的显著提高可能会成为现实,但这是否会被用来改善医患关系,或者促进其恶化,还有待观察。