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.
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.
While the health care industry can be notoriously sluggish in adopting new data tools, the potential for improving medical outcomes through data analysis is boundless, and efforts to enhance care quality via AI technologies are beginning to take root. Major tech companies – including Google, GE, and IBM – are on full alert, teaming up with care providers to explore ways that AI can solve health problems such as preventing relapse in the chronically ill or improving diagnostic accuracy in cancer patients. Several academic medical centers including University of Chicago Medicine and University of California San Francisco (UCSF) recently partnered with Google to apply machine learning to address population health needs. Google is developing tools to analyze large volumes of electronic health records (EHRs) and identify patient groups at risk of cardiac arrest, illness relapse, or other events, therefore reducing the likelihood of emergency hospital visits and inpatient stays. Care providers are under increasing pressure to cut costs and minimize adverse patient outcomes, and identifying high-risk populations and anticipating patient needs could provide the missing link to improve efficiency.