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AI Gold Seen in Healthcare’s Mountain of Waste

A new report estimates the cost of waste in the U.S. healthcare system alone ranges as high as $935 billion a year, about 25 percent of total healthcare spending.

A growing army of startups and established practitioners sees the inefficiencies as a trillion-dollar opportunity to apply AI.

The U.S. spends about 18 percent of its gross domestic product on healthcare, more than any other country. A report published online by the Journal of the American Medical Association surveyed 54 studies to estimate annual waste figures in six broad categories, including failures from choosing ineffective treatments (up to $166 billion), failures  from coordinating multiple treatments ($78 billion), fraud and abuse ($84 billion) and administrative complexity ($266 billion).

“Implementation of effective measures to eliminate waste represents an opportunity to reduce the continued increases in U.S. health care expenditures,” the report concluded.

MICCAI Heard the Call

Researchers echoed that theme at a major medical imaging conference in Shenzhen, China, recently.

Headshot Shiyuan Liu
Shiyuan Liu

Catherine Mohr, vice president of strategy at Intuitive Surgical, reviewed the history of medtech with an eye on “how to think about distinguishing price from value when developing the next generation of medical devices,” in a keynote at this year’s International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI).

Attendees also got an update on the state of the art in using AI in medical imaging in a keynote from Shiyuan Liu, president of the Chinese Medical Imaging AI Innovation Alliance. Liu called for practitioners, vendors and academics to work together to drive AI forward.

700+ AI Healthcare Startups

Opportunities span the waterfront. “Every single type of health professional” will be impacted by AI, said Eric Topol, founder and director of the Scripps Research Translational Institute, in a keynote at NVIDIA’s GTC event in Silicon Valley earlier this year. AI will help practitioners provide “better, faster, cheaper” care, said the author of the recently released book, “Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again.”

That message has not been lost on entrepreneurs. A recent healthcare event sponsored by a major Wall Street bank was “crawling with tech VCs, and five years ago that was not the case,” said Jeff Herbst, vice president of business development at NVIDIA.

With more than 700 startups, healthcare represents the largest category in NVIDIA’s Inception accelerator program that provides AI training and tools to fuel their growth. Herbst calls out Biotrillion as one to watch. The startup generates digital biomarkers to detect disease using its own analytics on sensor data from a user’s smartphone and smartwatch.

“The biggest opportunity in healthcare is in using AI to keep people well — this is the most exciting area to me,” he said.

There’s no shortage of other examples. San Francisco-based Fathom is developing deep learning tools to automate the painstaking medical coding process while increasing accuracy. Its tools use NVIDIA P100 and V100 Tensor Core GPUs in Google Cloud for both training and inference, reducing human time spent on medical coding by as much as 90 percent.

Houston-based InformAI helps reduce fatigue and stress for radiologists by building deep learning tools that can help them analyze medical scans faster. It’s image classifiers and patient outcome predictors run both on NVIDIA V100 GPUs in the Microsoft Azure cloud platform and an onsite NVIDIA DGX Station. In just 30 seconds they can analyze a patient’s 3D CT scan for 20 sinus conditions.

Subtle Medical of Menlo Park, CA, announced this week that it received FDA clearance for SubtleMR, its deep learning solution for improving the image quality of MRIs. The Inception member’s first product, SubtlePET, which can produce PET images in as little as a quarter of the scanning time of current systems, received FDA clearance last year. Both products are trained on DGX-1 and DGX Station and enabled by TensorRT.

Major Players Embrace AI

Medical imaging is one of the biggest areas in healthcare AI, with startups scattered around the globe. They include South Korean startup Lunit and InferVISION, one of China’s top medical imaging startups, focusing on lung nodule analysis and prediction from CT scans.

Major providers and vendors are also embracing AI. Two developers from UnitedHealth Group, one of the largest healthcare companies in the U.S., shared in a talk at GTC earlier this year how the provider is adopting AI for tasks that span prior authorization of medical procedures to directing phone calls.

In June, Siemens Healthineers and NVIDIA shared their latest work in AI for medical imaging at the Society for Imaging Informatics in Medicine annual conference. Siemens Healthineers is using an NVIDIA GPU-based supercomputing infrastructure to develop AI software for generating organ segmentations that enable precision radiation therapy.

“The area that will have the biggest impact in AI is healthcare,” said Ian Buck, vice president of NVIDIA’s Accelerated Computing Group in a recent interview.

“The healthcare industry is chock full of data … there are many obstacles ahead, but I am truly hopeful AI can help cure diseases and save lives — that makes me excited about the work we do,” Buck said.

The post AI Gold Seen in Healthcare’s Mountain of Waste appeared first on The Official NVIDIA Blog.


Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.