The world's population is expected to increase by one billion people by 2025, with nearly a third expected to be aged 65 and over. It is a testament to medical science that we are all living for longer, but whilst these advances have enabled us to spend more time enjoying retirement, Europe will increasingly be left with an ageing population – and this brings with it a set of challenges for healthcare systems and the patients they look after. Across Europe, there are a growing number of older people whose complex healthcare needs will have to be met. Cancer, in particular, presents a major concern. The data of the WHO (World Health Organization) show that in Europe, there are more than 3.7 million new cases and 1.9 million deaths from cancer each year1.
How healthcare has evolved from the first clinically useful image to a library of images analyzed by AI In August 1980, a team from Scotland made a breakthrough in imaging. Setting the stage for the widespread use of MRI scans, they obtained the first clinically useful image of a patient's internal tissues. Almost 30 years later, breakthroughs in imaging are becoming the normal.
There is no doubt that Hollywood has played a part in convincing society that artificial intelligence is a self-aware danger to humanity – but the underlying truth is much more mundane. In place of'power-hungry robots', AI systems are sophisticated algorithms that make sense of the 21st century's most precious commodity: Data. Drawing from test results and treatment histories, to lifestyle information and symptom diaries, AI algorithms are scanning these bottomless pits of information for clues and patterns that could revolutionise the way diseases are diagnosed, and care is delivered. The team at healthcare marketing agency Perfect Storm, Bristol, talk us through some of the ways in which AI is building the NHS of the future. Clinicians use a plethora of imaging tests, such as X-rays, CT scans and MRIs.
With artificial intelligence, machines can now examine thousands of medical images – and billions of pixels within these images – to identify patterns too subtle for a radiologist or pathologist to identify. The machine then uses this information to identify the presence of a disease or estimate its aggressiveness, likelihood of survival or potential response to treatment. We are engineers at the Center for Computational Imaging and Personalized Diagnostics. Our team works with physicians and statisticians to develop and validate these kinds of tools. Many worry that this technology aims to replace doctors.