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The Impact Of AI On The Future Of Radiology – 4 Scenarios In 2016, Professor Geoffrey Hinton, a neural network expert, said that we should stop training radiologists because in the next five years algorithms would be better than people in assessing medical images. It is 2021 and the demand for radiologists is growing, but it is too soon to say that Hinton was wrong. Geoffrey Everest Hinton did not pull his idea out of thin air. He is a cognitive psychologist and a computer scientist known for his work on artificial neural networks; he works in Google and at the University of Toronto. However, this renowned scientist did not consider
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three factors: the complex healthcare ecosystem, demand/supply of health services imbalance and the characteristics of doctors’ work. The US Bureau of Labor Statistics forecasts that the number of radiology and MRI technicians will grow by 7%
in 2019-2029. According to the American College of Radiology report, onethird of radiologists use AI algorithms at work these days. The FDA has already approved 80 radiological algorithms – about half of them are used in practice. Hinton did not consider that the demand for radiologists is growing, caused by the increase of screening programs and aging societies. According to the latest data, the NHS estimates that the shortage of radiologists amounts to about 30%, which translates into a deficit of approximately 1,800 employees. Despite these statistics, Hinton’s thesis is not necessarily wrong. If AI keeps improving at the same rate as it currently
