F013 What to expect from artificial intelligence in healthcare in the next 10 years?

“AI will bring a power shift in the consumer/payer/provider mix as consumers will take more and more control over their health and health care,” says Sally Daub, the CEO of Enlitic — the US startup using deep learning to distill actionable insights from billions of clinical cases and help doctors leverage the collective intelligence of the medical community.

Listen on Google Play Music on your android device, in iTunes or Podbean or use the RSS feed for your podcast player.

At the moment, the use of AI is highest in the field of medical imaging and diagnostics, drug discovery and therapy planning, but Accenture predicts that by 2026 150 billion US dollars could be saved annually due to applications to robot-assisted surgery, virtual nursing assistants, followed by administrative workflow assistance, fraud detection and dosage error reduction, to name the first few areas with most significant savings.

Healthcare AI is expanding at an annual rate of 40%, the global revenue generated by artificial intelligence systems will rise to $6.6 billion by 2021, according to Accenture.

One of the key targets Enlitic is focused on is early detection of lung cancer by combining biopsies along with existing medical data to be able to diagnose lung cancer earlier than with the traditional medical methods.

While based in San Francisco, Enlitic is present in Japan, Canada, Australia, and China.

AI is the buzzword startups are very keen on using when describing their products. We’ve been seeing ideas on what it could do in movies for decades. So what qualifies as AI? What are the dreams and what current reality around AI? How does AI look in practice?

Some of the questions addressed:

– What qualifies as AI? 
– What is the dream and what the current reality around AI? 
– How does AI look in practice? 
– How “seamless” is AI at the moment, how many developers does it require in Enlitic? 
– How is Enlitic incorporating a wide range of unstructured medical data, including radiology and pathology images, laboratory results such as blood tests and EKGs, genomics, patient histories, and electronic health records (EHRs) with deep learning technology?
– How many pilot projects and how many customers is Enlitic working with? 
– How big is the gap between the public expectations of AI and it’s actual capabilities?

Source link

Leave a Reply

Your email address will not be published.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

Pin It on Pinterest

Share This

Share this post with your friends!