Unsure of the correct platform you need to run Deep Learning experiments within the healthcare industry?

Here’s a recipe for you: GPU’s, healthcare, and a sprinkling of Deep Learning, equalling profound effects on how the pharmaceutical industry is being transformed. With Deep Learning, the industry is being revolutionised.

The health industry is facing the biggest change it has ever seen. The current success that Deep and Machine learning are having is truly remarkable. It seems we are on the edge of something truly amazing, something that will transform medicine and patient care for years to come.

However, with the media industry currently in a feeding frenzy with anything to with the words AI, how do you know what’s possible and what isn’t? What sort of software is required to enable my data to speak to me? From a hardware side, what GPU’s do I need for my workloads? Are my team skilled up on all the latest Algorithms and techniques?

It’s a massively interesting time to be involved in Deep Learning with so many new possibilities and ways of thinking. However, it can also be a very confusing time as well.

The advantages and disadvantages of Deep Learning have been extensively discussed in medical literature for some time now; with some sceptical, some worried of mass redundancies and some saying it will change the industry for the better. Either way, this space will remain focused on disruptive technologies and the benefits that they can bring.

Currently the field of healthcare is mainly focused in the following areas:

·        Diagnosis, detecting variations from the baseline data and comparing this to historical data.

·        Imaging diagnostics (Radiology and Pathology).

·        Targeted identifiers for early mapping of new diseases.

·        Big data analytics (supply, purchasing, bed waiting times).

·        Intelligent implants.

·        Robotics.

·        Drug discovery.

The above list is just some of the very interesting areas that Deep Learning can have a positive effect. But don’t worry, this does not mean anytime soon that AI will replace human physicians or the ability for professionals to make the final decision. AI initially will be introduced more as an aid as opposed to anything else. It will augment the skills of the physicians and by using techniques like Deep Learning will be able to offer more insights than by the physicians alone. Overtime the interaction between human physician and AI powered diagnostics and analytics will enhance these systems and the accuracy of the experiments they run, eventually getting to a point where customer confidence is so high that we may start to delegate entire tasks to AI systems from diagnosis to operation’s using robotics. However, we are not there yet and for this to happen there must be continued investment.

Currently within the health industry the following are techniques most commonly used (examples only), linear regression, support vector machine, neural networks, logistic regression, na?ve Bayes, nearest neighbour, random forest, decision tree and discriminant analysis. Unsupervised learning is used but more in trying to reduce dimensionality or to help identify subgroups within the data, which of course helps with supervised learning. Novatech has support measures in place to ensure that we are able to support customers with workshops and training based around the above.

In relation to the engines that power deep learning, this sits squarely with NVIDIA and their impressive collection of deep learning GPU’s. Regardless of the workloads you are running, chances are that you will be running those experiments on NVIDIA GPU’s. Titan series GPU’s are typically seen most in development workstations, with enterprise grade, more densely packed GPU’s being deployed for incredibly large and complicated workloads typically around training. In relation to this, if you have read any of my previous posts you will know how fundamentally important it is to the decision-making progress to understand your workloads and match them with the correct GPU’s to power those workloads.

There is also the case of supplying enterprise grade equipment with enterprise grade support features. This is incredibly important currently as the investment levels from the Health sector into Deep Learning is only going to increase, so those companies with a historically fantastic Enterprise support system will benefit. Speak to the team to understand the different levels of enterprise support offered by Novatech and our partners.

If you would like to understand the use cases of our clients within the health industry and see what companies are able to achieve using Deep Learning, then please contact myself directly via Linkedin or go to the Novatech.co.uk/deeplearning. We are also able to put you into direct contact with Novatech’s clients who we have deployed Deep Learning systems, so that you can hear directly from them the benefits that they received from the recommended systems we supplied, and the consultancy services offered.