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January 1/8, 2019

Questions for Artificial Intelligence in Health Care

Author Affiliations
  • 1Healthcare Innovation Lab, BJC Healthcare/Washington University School of Medicine, St Louis, Missouri
  • 2Division of Cardiology, Washington University School of Medicine, St Louis, Missouri
  • 3American College of Cardiology, Washington, DC
  • 4Institute for Informatics, Washington University School of Medicine, St Louis, Missouri
JAMA. 2019;321(1):31-32. doi:10.1001/jama.2018.18932

Artificial intelligence (AI) is gaining high visibility in the realm of health care innovation. Broadly defined, AI is a field of computer science that aims to mimic human intelligence with computer systems.1 This mimicry is accomplished through iterative, complex pattern matching, generally at a speed and scale that exceed human capability. Proponents suggest, often enthusiastically, that AI will revolutionize health care for patients and populations. However, key questions must be answered to translate its promise into action.

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    2 Comments for this article
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    AI in Public Health
    Carol Schlismann, MSN |
    Could AI be a good tool for identifying index cases and patterns of infection? If public health organizations' data were assembled quickly, AI might be able to assist with finding the natural history of an epidemic more quickly, possibly making a faster, more focused response. Using AI to analyze data from earlier outbreaks might be of interest for prediction.

    Hospitals could perhaps use AI for infection prevention and monitoring. Key items in the EMR could trigger AI to "drill down" to rule possible hospital-acquired infections, find patterns, and plan a good response for future prevention efforts.
    CONFLICT OF INTEREST: None Reported
    Moving From Probabilistic Approaches to Causal Inference Models in AI
    Sandeep Reddy, MBBS, PhD | Deakin University School of Medicine
    Dear Editor(s),

    I agree with the author's viewpoints about the current limitations with AI techniques that depend on probabilistic or classifier approaches. I have previously argued for a causal inference AI approach in Medicine in other forums: https://www.linkedin.com/pulse/moving-from-probability-inference-causal-hybrid-approaches-reddy/

    However, I would not be pessimistic about AI and rule out the immense potential AI techniques have in the field of medicine, in clinical care delivery in particular. This optimism exists even with the current limitations of AI processes. With the preponderance of big data in medicine, the increasing costs of delivering medical care, and increasing medical errors, AI techniques
    present a never before available option to address these issues. Also, the successes of AI techniques medicine are not a fluke or mere hype; the usefulness of these approaches is being consistently documented in research journals.
    CONFLICT OF INTEREST: None Reported
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