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    AI has brains—but it’s no MD


    Watson is just one example of how artificial intelligence (AI) is being utilized for clinical healthcare decisions, but much of the criticism is that Watson is being created outside of the clinical arena rather than being developed side-by-side with clinicians.4

    Google’s DeepMind has announced a 5-year agreement with a UK National Health Service (NHS) trust that will give it access to patient data to develop and deploy its healthcare app, called Streams.5 According to Google, “We’ve built Streams in close partnership with NHS clinicians, who know exactly what they need.”

    And on the device side of AI integration, Siemens has boldly said that their vision is “to equip a combined mammography and ultrasound device with software that not only increases the informative value of the mammogram, but also integrates data about a patient’s individual cancer risk.”6

    As in the example above of the young woman with cancer, AI should be used as a clinical decision aid and not a replacement for clinical decision-making.

    AI is helping us understand diseases and conditions in ways that were once impossible. For example, researchers at the University of California-San Francisco are participating in the government-funded multisite clinical trial TRACK-TBI (Transforming Research and Clinical Knowledge in Traumatic Brain Injury), for which they have developed new diagnostic and prognostic markers to help refine outcome assessments.7 The authors state that the different ways secondary brain injuries manifest is a significant obstacle to the development of new treatments. This is real-life precision medicine, because the data inputs are vague at best, and the output is a highly accurate prognosis.

    Another example is the increasing phenomenon of polypharmacy—patients taking more than one prescription drug. With recent advances in genetic drug susceptibility screening, robust drug-to-drug interaction data, and improving longitudinal population data, AI is able to help us not only pick the right drugs for our patients, but also help prevent untoward outcomes from picking the wrong drug.

    In 2016, it was estimated that nearly 250,000 Americans die each year from medical errors, which makes them the third-leading cause of death in the United States.8 Number 1 was heart disease and number 2 was cancer, both of which claimed approximately 600,000 lives. If we limited the scope of AI to only cardiovascular, oncology, and hospital-admitted patients, it’s plausible that AI could help save more than a million lives a year.

    Brian A. Levine, MD, MS, FACOG
    Dr. Levine is Practice Director at the Colorado Center for Reproductive Medicine, New York, New York.


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