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Artificial intelligence (AI) and cancer prevention: the potential application of AI in cancer control programming needs to be explored in population laboratories such as COMPASS.

Understanding the risk factors that initiate cancer is essential for reducing the future cancer burden. Much of our current cancer control insight is from cohort studies and newer large-scale population laboratories designed to advance the science around precision oncology. Despite their promise for...

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Published in:Cancer Causes & Control Vol. 30; no. 7; pp. 671 - 676
Main Authors: Leatherdale, Scott T., Lee, Joon
Format: Journal Article
Published: Springer Nature Jul2019
Online Access:View this record in EBSCOhost
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      dt: Jul2019
      vid: 30
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      pub: Springer Nature
      place: New York, New York
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        atl: Artificial intelligence (AI) and cancer prevention: the potential application of AI in cancer control programming needs to be explored in population laboratories such as COMPASS.
      aug:
        au:
          Leatherdale, Scott T.
          Lee, Joon
        affil: School of Public Health and Health Systems, University of Waterloo, 200 University Avenue, N2L 3G1, Waterloo, ON, Canada
      sug:
        subj:
          Artificial Intelligence
          Neoplasms Prevention and Control
          Preventive Health Care
          Algorithms
          Impact of Events Scale
      ab: Understanding the risk factors that initiate cancer is essential for reducing the future cancer burden. Much of our current cancer control insight is from cohort studies and newer large-scale population laboratories designed to advance the science around precision oncology. Despite their promise for improving diagnosis and treatment outcomes, their current reductionist focus will likely have little impact shifting the cancer burden. However, it is possible that these big data assets can be adapted to have more impact on the future cancer burden through more focus on primary prevention efforts that incorporate artificial intelligence (AI) and machine learning (ML). ML automatically learns patterns and can devise complex models and algorithms that lend themselves to prediction in big data, revealing new unexpected relationships and pathways in a reliable and replicable fashion that otherwise would remain hidden given the complexities of big data. While AI has made big strides in several domains, the potential application in cancer prevention is lacking. As such, this commentary suggests that it may be time to consider the potential of AI within our existing cancer control population laboratories, and provides justification for why some small targeted investments to explore their impact on modelling existing real-time cancer prevention data may be a strategic cancer control opportunity.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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