This imbalance between supply and demand is the underlying cause of the organ shortage crisis, with more than 100,000 patients currently waiting for a solid organ transplant in the US alone. It also forces us to rely heavily on animals in medical research, a practice that can't replicate major aspects of human physiology and makes it necessary to inflict harm on sentient creatures. In addition, the safety and efficacy of any experimental drug must still be confirmed in clinical trials on living human bodies. These costly trials risk harm to patients, can take a decade or longer to complete, and make it through to approval less than 15% of the time. There might be a way to get out of this moral and scientific deadlock.

Doc.ai is hoping to revolutionize the medical industry by bringing AI doctors to all through their smartphones. Using Blockchain technology, the platform will be able to collect masses of medical data globally and generate insights from that information. Furthermore, through machine learning, the data collected will be analyzed and processed in order to provide personalized feedback to users about their own medical issues. Doc.ai's platform aims to provide users with the ability to essentially call on a doctor in their pocket. However, because medical practitioners are in short supply, difficult to access and expensive, this company is looking at powerful technologies rather than humans.

Kimberly Powell, who leads Nvidia's efforts in health care, says the company is working with medical researchers in a range of areas and will look to expand these efforts in coming years. Most notably, a machine-learning technique called deep learning is being applied to processing medical images and sifting through large amounts of medical data. Nvidia is, for example, working with Bradley Erickson, a neuro-radiologist at the Mayo Clinic, to apply deep learning to brain images. There are, however, significant challenges in applying techniques like deep learning to medicine.

The chip maker Nvidia is riding the current artificial-intelligence boom with hardware designed to power cutting-edge learning algorithms. And the company sees health care and medicine as the next big market for its technology. Kimberly Powell, who leads Nvidia's efforts in health care, says the company is working with medical researchers in a range of areas and will look to expand these efforts in coming years. "There's this amazing surge in medical imaging research," Powell said at MIT Technology Review's EmTech Digital conference in San Francisco on Monday. Most notably, a machine-learning technique called deep learning is being applied to processing medical images and sifting through large amounts of medical data.

Doctors will one day be able to more accurately predict how long patients with fatal diseases will live. Medical systems will learn how to save money by skipping expensive and unnecessary tests. Radiologists will be replaced by computer algorithms. These are just some of the realities patients and doctors should prepare for as "machine learning" enters the world of medicine, according to Dr. Ziad Obermeyer, an assistant professor at Harvard Medical School, and Dr. Ezekiel Emanuel of the University of Pennsylvania, who recently coauthored an article in the New England Journal of Medicine on the topic. But what exactly is "machine learning"?

Doctors will one day be able to more accurately predict how long patients with fatal diseases will live. Medical systems will learn how to save money by skipping expensive and unnecessary tests. Radiologists will be replaced by computer algorithms. These are just some of the realities patients and doctors should prepare for as "machine learning" enters the world of medicine, according to Dr. Ziad Obermeyer, an assistant professor at Harvard Medical School, and Dr. Ezekiel Emanuel of the University of Pennsylvania, who recently coauthored an article in the New England Journal of Medicine on the topic. But what exactly is "machine learning"? And how will medical systems make use of it?

Computing science researcher Siamak Ravanbakhsh (middle), with his co-supervisors Russell Greiner (left) and David Wishart. Ravanbakhsh has developed a computer system called Bayesil that could dramatically improve diagnosis and treatment of a wide spectrum of diseases. Siamak Ravanbakhsh, who recently completed his PhD in computing science at the University of Alberta and whose research was recently published in the scientific journal PLOS ONE, said Bayesil, the computer application resulting from this breakthrough, is pretty easy to explain in basic terms. "There is this technology called NMR spectrometry, which uses some of the same physical principles as MRI. This technology is very cool, because it can determine the concentration of certain compounds in your body," he said.