High-performance medicine: the convergence of human and artificial intelligence.
Clicks: 236
ID: 51762
2019
Article Quality & Performance Metrics
Overall Quality
Improving Quality
0.0
/100
Combines engagement data with AI-assessed academic quality
Reader Engagement
Steady Performance
71.2
/100
236 views
189 readers
Trending
AI Quality Assessment
Not analyzed
Abstract
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.
Reference Key |
topol2019highperformancenature
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
---|---|
Authors | Topol, Eric J; |
Journal | Nature Medicine |
Year | 2019 |
DOI | 10.1038/s41591-018-0300-7 |
URL | |
Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.