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.
Keywords
WorkflowCloud computingTransparency (behavior)Computer scienceBig dataProductivityData scienceDeep learningArtificial intelligenceProcess (computing)Precision medicineRisk analysis (engineering)MedicineComputer securityData miningPathology
MeSH Terms
AlgorithmsArtificial IntelligenceData AnalysisDeep LearningHumansMedicinePhysicians
Affiliated Institutions
Related Publications
A survey on Image Data Augmentation for Deep Learning
Abstract Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfi...
Generative Adversarial Networks: An Overview
Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data. They achieve this by deriving backpropagation sig...
Representation Learning: A Review and New Perspectives
The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more...
Gaussian Processes for Machine Learning
We give a basic introduction to Gaussian Process regression models. We focus on understanding the role of the stochastic process and how it is used to define a distribution over...
LRBM: A Restricted Boltzmann Machine Based Approach for Representation Learning on Linked Data
Linked data consist of both node attributes, e.g., Preferences, posts and degrees, and links which describe the connections between nodes. They have been widely used to represen...
Publication Info
- Year
- 2018
- Type
- review
- Volume
- 25
- Issue
- 1
- Pages
- 44-56
- Citations
- 6557
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
6557
OpenAlex
173
Influential
Cite This
Eric J. Topol
(2018).
High-performance medicine: the convergence of human and artificial intelligence.
Nature Medicine
, 25
(1)
, 44-56.
https://doi.org/10.1038/s41591-018-0300-7
Identifiers
- DOI
- 10.1038/s41591-018-0300-7
- PMID
- 30617339