There are numerous examples of Artificial Intelligence (AI) being used to accelerate everything from computational drug discovery through to medical imaging analysis. It has become a fundamental requirement in healthcare acknowledged by both Governments and Professional bodies. I would go as far as to proffer that AI will be a defining factor in whether we give our healthcare professionals the ability to provide the services in key areas such as Neurology.

According to the Nuffield Trust, in 1950, the UK NHS spent an estimated £460 million. The estimate for 2020 is 340 times that amount – sitting at approximately £158 billion, which represents over 7% of GDP.

If we look at Neurology, consistent pressures on staff, costs, processes, and a huge surge in analytical data the picture starts to form. Mix this with an increase of 10 years on the average life expectancy since 1950 and the inextricable links to neurological disorders such as Alzheimer’s and Parkinson’s diseases and you have part of the bigger picture. Sharpen this further with the shortage of radiologists across the globe and you have a perfect storm brewing.

However, there is hope. The development of AI specific technologies by the likes of NVIDIA coupled with advances in processors, Deep Learning frameworks and applications that can now acquire image data with greater resolution – all allow for more accurate model training through gradient learning. AI applications can identify (with a very high level of accuracy), both benign and malignant cancerous cells which are then validated by radiologists. As the model is trained further and the number of varying images increases the accuracy will grow to a point where the validation becomes an ethical question rather than one related to accuracy.

This is a necessity as image data and workloads are increasing at such a rate that the existing processes and infrastructure will not be able to sustain it. We do not have, nor can we train, enough radiologists to cope with this data growth which is why AI technology will play a starring role in this field.

vScaler can run AI frameworks accelerated by GPUs in a cloud environment on or off premise and store your data securely in a highly optimised, cost-effective platform. If you want to know more or try the solution for yourself, please contact

Leave a Reply

Your email address will not be published. Required fields are marked *