Over the past few years, headlines suggesting the demiseof radiology under the rise of artificial intelligence (AI) havespread in specialized and lay media. One of the most emblematictestimonials came from Professor Geoffrey Hinton of theUniversity of Toronto, an iconic researcher on neural networksand machine learning. In 2016, professor Hinton stated that“people should stop training radiologists now” and “it is justcompletely obvious that within 5 years deep learning will do betterthan radiologists”(1). The impact of the bad news has beennoticed by medical students. A considerable proportion of medicalstudents have been discouraged from considering radiologysolely because of the uncertain impact of AI on the field(2,3).Indeed, the notion that AI may negatively impact radiology inthe near future permeates the medical imaging community(4).When such discussion emerges, many of us forget one of themost important factors when analyzing the potential demise ofradiology in the face of AI: radiology is an infinite game.
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