• NEWS FEATURE

An AI revolution is brewing in medicine. What will it look like?

Emerging generalist models could overcome some limitations of first-generation machine-learning tools for clinical use.
By
  1. Mariana Lenharo
    View author publications

    You can also search for this author in PubMed  Google Scholar

CT scans of the lungs of a patient with interstitial lung disease

Researchers are feeding machine-learning tools millions of medical scans to give them general diagnostic capabilities. Credit: Massimo Brega/Science Photo Library

Access through your institution
Buy or subscribe

Jordan Perchik started his radiology residency at the University of Alabama at Birmingham near the peak of what he calls the field’s “AI scare”. It was 2018, just two years after computer scientist Geoffrey Hinton had proclaimed that people should stop training to be radiologists because machine-learning tools would soon displace them. Hinton, sometimes referred to as the godfather of artificial intelligence (AI), predicted that these systems would soon be able to read and interpret medical scans and X-rays better than people could. A substantial drop in applications for radiology programmes followed. “People were worried that they were going to finish residency and just wouldn’t have a job,” Perchik says.

Access options

Access through your institution

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Nature 622, 686-688 (2023)

doi: https://doi.org/10.1038/d41586-023-03302-0

References

  1. Chen, M. et al. Front. Med. 9, 990604 (2022).

    Article  Google Scholar 

  2. Strohm, L., Hehakaya, C., Ranschaert, E. R., Boon, W. P. C. & Moors, E. H. M. Eur. Radiol. 30, 5525–5532 (2020).

    Article  PubMed  Google Scholar 

  3. Liu, X. et al. Lancet Digit. Health 1, E271–E297 (2019).

    Article  PubMed  Google Scholar 

  4. Zhou, Y. et al. Nature 622, 156–163 (2023).

    Article  PubMed  Google Scholar 

  5. Azizi, S. et al. Nature Biomed. Eng. 7, 756–779 (2023).

    Article  PubMed  Google Scholar 

  6. Tu, T. et al. Preprint at https://arxiv.org/abs/2307.14334 (2023).

  7. Li, C. et al. Preprint at https://arxiv.org/abs/2306.00890 (2023).

  8. Poplin, R. et al. Nature Biomed. Eng. 2, 158–164 (2018).

    Article  PubMed  Google Scholar 

Download references

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