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Predictable response: Finding optimal drugs and doses using artificial intelligence

Nature Medicine volume 23, pages 1244–1247 (2017)Cite this article

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A Correction to this article was published on 07 December 2017

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The e-mail that he had been waiting for was the first of a series of messages that would arrive over the next ten days, detailing how much and how many medicines each patient was receiving, along with blood-test results showing how quickly the various drugs were being metabolized. Ho's collaborators—including his son, Dean Ho, a biomedical engineer also at UCLA—hoped that by using data collected over the ten-day period, they could then predict how much of the drug each patient would need going forward, and thereby avoid unwanted side effects. But rather than attempting these calculations in the conventional way, they used a new tool: artificial intelligence (AI).

At the end of their data-collection period, the Hos and their collaborators were finally ready to start helping the physicians with their dilemma. By using a mathematical equation developed using artificial intelligence, they had calculated the proper dosage to give to the four patients in the experimental arm of the trial. The results were graphs—one for each of these four participants—that showed a smooth 'peak,' clearly delineating the best possible dosage for the individuals. The Hos reveled in having their work validated, recalls Dean, who is co-director of the Weintraub Center for Reconstructive Biotechnology at UCLA. “We knew that artificial intelligence would find the optimum for these patients,” he says.

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Change history

  • 22 November 2017

    In the November 2017 issue, the story "Predictable Response: Finding optimal drugs and doses using artificial intelligence" (Nat. Med. 23, 1244–1247, 2017) misspelled Josep Bassaganya-Riera's last name in the second mention of his work. His last name is spelled "Bassaganya-Riera" and not "Bassanganya-Riera". The error has been corrected in the HTML and PDF versions of the article as of 22 November 2017.

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  1. Shraddha Chakradhar is Nature Medicine's associate news editor in Boston.

    • Shraddha Chakradhar
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  1. Shraddha Chakradhar
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Chakradhar, S. Predictable response: Finding optimal drugs and doses using artificial intelligence. Nat Med 23, 1244–1247 (2017). https://doi.org/10.1038/nm1117-1244

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