Humanity has a problem: we are living longer. Between 2000 and 2015, global average life expectancy increased by five years, to 71.4. This is the fastest pace since the 1960s, according to the World Health Organisation (WHO).

This is a problem because long life tends to be expensive. Those surviving conditions that would once have killed often need continuous care, and older people generally need more treatment for ever more complex conditions that emerge in later life.

This, along with rising costs of new medicines and procedures, has caused ”healthcare inflation” in excess of that seen in other industries. Between 2011 and 2016, average US family health premiums bought through employers increased by 20%, according to figures from PwC, while wages rose by just 11%. WHO data show healthcare spending as a proportion of national economies rising by a third in the UK between 2000 and 2015, by half in Japan and by two-thirds in the US. In many countries, the result is a full-blown crisis.

Many sectors faced with rising costs have used technology to do much more with far less, but healthcare has often proved resistant

“The slowest sector to respond to technology is, unfortunately, healthcare,” says Rami Qahwaji, professor of visual computing at the University of Bradford in the UK, where he helps run a government-funded digital health enterprise zone.

Despite healthcare’s resistance, Mr Qahwaji thinks there are significant opportunities.

Among the most promising is the adoption of artificial intelligence (AI), which could significantly lower the burden on doctors by doing some of their work for them. Owing to advances in machine learning, which learn by a wealth of sample data to make predictions, technologists have made significant leaps in the kinds of perceptual, pattern-recognition tasks that constitute significant parts of the job of diagnosis in radiology, pathology, dermatology and other specialisms.

For the time being, AI systems will support human clinicians rather than replace them. AI is good at carrying out very specific tasks, says Elad Walach, chief executive and founder of Aidoc Medical, an Israeli start-up that has developed a system for spotting abnormal radiology scans of the head and neck, which has been adopted by healthcare providers across Europe, Israel and the US.

“But radiology diagnosis is not a narrow task,” he says. The aim is to let the software do some of the more laborious work and make better use of radiologists’ time. Aidoc’s technology supports radiologists’ decision-making by prioritising cases that appear to include abnormalities and indicates where these lie within the images, but it leaves the decisions to the radiologists.

Walach says that one facility has reduced the time clinicians spent on scanning and diagnosis by 60%, adding that other areas with potential for using AI include analysis of genetic material. In the long term, it is likely that machine learning approaches will encroach on tasks based on perception, interpreting data and prediction that make up a large portion of doctors’ jobs. This is good news for healthcare budgets.

There could be more general efficiency benefits from the adoption of AI in triaging patient complaints. Suitably informed algorithms can direct people to the most appropriate level of healthcare for their symptoms, with research suggesting that a fifth of UK visits to general practitioners and emergency departments concern minor problems that could be treated at home. Given the savings that could be realised by freeing up such facilities, innovation charity Nesta believes that it is likely that AI systems will become common as first points of contact for healthcare systems.

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