High-performance medicine: the convergence of human and artificial intelligence
- PMID: 30617339
- DOI: 10.1038/s41591-018-0300-7
High-performance medicine: the convergence of human and artificial intelligence
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.
Comment in
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ChatGPT: Impact of an artificial author on bibliometrics.Indian J Med Ethics. 2023 Apr-Jun;VIII(2):93-94. doi: 10.20529/IJME.2023.029. Indian J Med Ethics. 2023. PMID: 37401180
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References
-
- Thakrar, A. P. et al. Child mortality in the US and 19 OECD comparator nations: a 50-year time-trend analysis. Health Aff. (Millwood) 37, 140–149 (2018).
-
- Roser, M. Link between health spending and life expectancy: US is an outlier. In Our World in Data https://ourworldindata.org/the-link-between-life-expectancy-and-health-s... (2017).
-
- Berwick, D. M. & Hackbarth, A. D. Eliminating waste in US health care. JAMA 307, 1513–1516 (2012). - PubMed
-
- Wang, X. et al. ChestX-ray8: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. Preprint at https://arxiv.org/abs/1705.02315 (2017).
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