With AI, the solution is held in its capability to draw “intelligent” inferences based on vast amounts of raw data. Smart devices and robotics are already making tentative inroads to the healthcare marketplace. It is seen with AI across the globe: 63% of healthcare executives worldwide already actively invest in AI technologies, and 74% say they are planning to do so.

The data that fuels AI is at the heart of today’s healthcare delivery, and as more information goes online, it continues to proliferate. Between 2010 and 2015, the amount of stored patient data increased 700%. It is this vast data that is currently being utilised by companies to bring new drugs and cures to patients across the world, or for bots to self-diagnose individuals from the comfort of their own home, leading to less busy hospitals and GP’s. The healthcare industry is currently one of the biggest areas of investment for deep learning and continues to grow and help save lives through its adapting usage.

Current and future uses and benefits.

Today, Deep Learning is an established technology that is being employed in a number of areas:

  • VR:

    Virtual reality assistants, learning from top surgeons identifying best practices to make VR models more accurate.

  • Imaging analytics:

    Using images to diagnose cancer tumours or other elements. In some cases the AI based programmes show better than human error rates.

  • Drug creation:

    Deep Learning in drug creation can massively speed up the process of discovery by running simulations and decoding data faster. Typically this process can take years or even decades.

  • Individual health mapping:

    Predicting the likelihood of an individuals chances of acquiring chronic conditions by data and intervening earlier to prevent illness.

  • Managing medical data:

    Data management is the most widely used application of AI and digital automation.

  • Automation of repetitive jobs:

    Data entry, CT scans, analysing tests, X-rays to name just a few.

  • Treatment plans:

    Using intelligent systems to analyze data to make not only a diagnosis but also to make a treatment plan.

  • Virtual nurses:

    Systems capable of answering questions about symptoms, diagnosing illnesses, organising for a doctor or hospital visit.

  • Health monitoring:

    Wearable health trackers. processing real time information on users then sharing this information with doctors.

  • Robotic surgery:

    Using robots and DL together to advance areas of surgery (The Da Vinci robot).