Arterys, a medical imaging system, reads MRIs of the heart and measures blood flow through its ventricles. The process usually takes a trained professional forty-five minutes, but Arterys can do the same task in around fifteen seconds. Incredibly, it has a self-teaching neural network which constantly adds to its knowledge of how the heart works with each new case it examines. It is in areas such as this where automation will make initial incursions into medicine, boosting productivity by accompanying, rather than replacing, existing workers. Yet such systems will improve with each passing year and some, like ‘godfather of deep learning’ Geofrey Hinton, believe that medical schools will soon stop training radiologists altogether.
Perhaps that is presumptuous – after all, we ’d want a level of quality control and maybe even the final diagnosis to involve a human – but even then, this massively upgraded, faster process might need one trained professional where at present there are dozens, resulting in a quicker, superior service that costs less in both time and money. In an ageing society such advantages won’t just be welcome, they’ll be necessary.
A similar phenomenon is unfolding with law and legal services – a historically middle-class part of the service economy. According to a 2016 study by the consultancy Deloitte, 114,000 legal jobs in Britain – around 40 per cent of the entire sector – are likely to be automated over the next twenty years. That same study found technology had already eliminated 31,000 jobs in the industry. These tended to be lower-level positions, as intelligent search systems are increasingly preferred to junior lawyers and paralegals in a number of areas, especially those most engaged in repetitive searches or processing extraordinarily large amounts of information.
Full Automation
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