Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • NEWS AND VIEWS

Deep learning detects impending organ injury in the clinic

Organ damage is often detected late, when treatment options are limited. The use of artificial intelligence to continuously monitor a patient’s medical data can identify people at risk of imminent kidney injury.
  1. Eric J. Topol

    1. Eric J. Topol is in the Department of Molecular Medicine, Scripps Research Translational Institute, La Jolla, California 92037, USA.

    View author publications

    You can also search for this author in PubMed  Google Scholar

Acute injury to the kidneys occurs in one in five patients in US hospitals1. It is a common condition in hospital patients because it can be caused by a number of factors, including abnormal blood pressure or blood volume. But the ability to predict whether or when acute kidney injury will happen is limited. For people who are at high risk of developing this condition, the standard clinical approach is daily assessment of their laboratory test results, including the concentration of creatinine in their blood, because high levels of this molecule are a hallmark of kidney problems.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

Buy

All prices are NET prices.

Nature 572, 36-37 (2019)

doi: https://doi.org/10.1038/d41586-019-02308-x

References

  1. Wang, H. E., Muntner, P., Chertow, G. M. & Warnock, D. G. Am. J. Nephrol. 35, 349–355 (2012).

    PubMed  Article  Google Scholar 

  2. Tomašev, N. et al. Nature 572, 116–119 (2019).

    Article  Google Scholar 

  3. Mohamadlou, H. et al. Can. J. Kidney Health 5, https://doi.org/10.1177/2054358118776326 (2018).

    Article  Google Scholar 

  4. Pan, Z. et al. Preprint at https://arxiv.org/abs/1901.04364 (2019).

  5. Keane, P. A. & Topol, E. J. NPJ Dig. Med. 1, 40 (2018).

    Article  Google Scholar 

  6. Rajkomar, A. et al. NPJ Dig. Med. 1, 18 (2018).

    Article  Google Scholar 

Download references

Competing Interests

E.J.T. is a member of the Verily Advisory Board.

Subjects

Latest on:

Nature Careers

Jobs

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing

Search

Advanced search

Quick links