SUSTAINABILITY

Medicine Sustainability Report

DEEP LEARNING

ADVANCES IN MEDICINE

Just What the Doctor Ordered:
Improving Patient Care with AI

Artificial Intelligence is transforming the world of medicine. AI can help doctors make faster, more accurate diagnoses. It can predict the risk of a disease in time to prevent it. It can help researchers understand how genetic variations lead to disease.

Although AI has been around for decades, new advances have ignited a boom in deep learning. The AI technique powers self-driving cars, super-human image recognition, and life-changing—even life-saving—advances in medicine.

Deep learning helps researchers analyze medical data to treat diseases. It enhances doctors’ ability to analyze medical images. It’s advancing the future of personalized medicine. It even helps the blind “see.”

“Deep learning is revolutionizing a wide range of scientific fields,” said Jen-Hsun Huang, NVIDIA CEO and co-founder. “There could be no more important application of this new capability than improving patient care.”

Three trends drive the deep learning revolution: more powerful GPUs, sophisticated neural network algorithms modeled on the human brain, and access to the explosion of data from the internet (see “Accelerating AI with GPUs: A New Computing Model”).

Mining Medical Data for Better, Quicker Treatment

Medical records such as doctors' reports, test results and medical images are a gold mine of health information. Using GPU-accelerated deep learning to process and study a patient's condition over time and to compare one patient against a larger population could help doctors provide better treatments.

Projects in the forefront include:

  • Massachusetts General Hospital's new clinical data science center will compare a patient's tests and history with data from a vast population of other patients to improve detection, diagnosis, treatment, and management of disease. NVIDIA is the founding technology partner of the center, which will use our NVIDIA DGX-1 deep learning supercomputer to deliver insights.
  • By analyzing electronic health records, researchers from Sutter Health and the Georgia Institute of Technology showed they could predict heart failure as much as nine months before doctors using traditional means.
  • At Children's Hospital Los Angeles, scientists are gaining insights from 13,000 patient snapshots and 10 years of electronic health records to provide more effective drug treatments in the hospital's pediatric intensive care unit.
Better, Faster Diagnoses

Medical images such as MRIs, CT scans, and X-rays are among the most important tools doctors use in diagnosing conditions ranging from spine injuries to heart disease to cancer. However, analyzing medical images can often be a difficult and time-consuming process.

Researchers and startups are using GPU-accelerated deep learning to automate analysis and increase the accuracy of diagnosticians:

  • Imperial College London researchers hope to provide automated, image-based assessments of traumatic brain injuries at speeds other systems can't match.
  • Behold.ai is a New York startup working to reduce the number of incorrect diagnoses by making it easier for healthcare practitioners to identify diseases from ordinary radiology image data.
  • Arterys, a San Francisco-based startup, provides technology to visualize and quantify heart flow in the body using any MRI machine. The goal is to help speed diagnosis.
  • San Francisco startup Enlitic analyzes medical images to identify tumors, nearly invisible fractures, and other medical conditions.
Genomics for Personalized Medicine

Genomics data is accumulating in unprecedented quantities, giving scientists the ability to study how genetic factors such as mutations lead to disease. Deep learning could one day lead to what’s known as personalized or “precision” medicine, with treatments tailored to a patient’s genomic makeup.

Although much of the research is still in its early stages, two promising projects are:

  • A University of Toronto team is advancing computational cancer research by developing a GPU-powered “genetic interpretation engine” that would more quickly identify cancer-causing mutations for individual patients.
  • Deep Genomics, a Toronto startup, is applying GPU-based deep learning to understand how genetic variations lead to disease, transforming personalized medicine and therapies.
Deep Learning to Aid Blind People

Nearly 300 million people worldwide struggle to manage such tasks as crossing the road, reading a product label, or identifying a face because they’re blind or visually impaired. Deep learning is beginning to change that.

Horus Technology, winner of NVIDIA’s first social innovation award at the 2016 Emerging Companies Summit, is developing a wearable device that uses deep learning, computer vision, and GPUs to understand the world and describe it to users.

One of the early testers wept after trying the headset-like device, recalled Saverio Murgia, Horus CEO and co-founder. “When you see people get emotional about your product, you realize it’s going to change people’s lives.”