In this 2017 article, Dr. Cosima Gretton says that as AI use becomes more prevalent in health care, more errors might occur. She attributes this increase in errors to automation bias, or the tendency to not search for contradictory information in light a computer-generation solution. And yet we know that the quality of AI relies on the quality of data it has been trained with. It is important for medical personnel to really look at the results, especially with most AI tech still in its infancy stage.
Moreover, with the AI blackbox still unresolved [see article on The AI Blackbox And Human Deliberative Processes to understand what this concept is], wide-scale automation bias could be catastrophic.
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