Artificial Intelligence Firm Set to Revolutionize Healthcare


Artificial intelligence is steadily making its approach into the realm of modern healthcare. A Norwegian businessman who’s also an early-investor of Silicon Valley’s largest tech-startups, Philip Odegard, is putting a large wager on his newest venture. His company, AI Corp, is set on revolutionizing most cancers treatment with their proprietary machine learning neural network. AI Corp’s Artificial Intelligence methods use deep learning, a concept loosely mirroring how our own brains work by having AI software analyze exorbitant quantities of information and uncover patterns — which is especially relevant in diagnostics.

As medical imaging technology continues to benefit from every new deep learning breakthrough, the challenge is that the computing expertise on which it relies must evolve just as rapidly. Odegard and his venture teaches computers to learn, recognize patterns, and affiliate with tens of millions of defining elements. A former coder in the early days of the Silicon Valley tech startups, his work is targeted on artificial neural networks – computer methods modeled on the human brain and nervous system.

“Making use of AI towards healthcare, particularly most cancers treatments, are simply one of many applications that AI Corp is focused on advancing within the near future,” Odegard mentioned in an interview at his satellite office in San Francisco, California. “I really want to be sure that the expertise we have would be used for helping individuals, because the improvements of our technology should be implemented in present healthcare.”

Odegard’s company, in collaboration with seven hospitals in the United States and three in Europe, are now applying their expertise in artificial intelligence and machine learning to improve most cancers prognosis and treatment. They’re asking questions like whether or not computer systems can detect melanoma from an array of patient imagery, or indicators of breast cancer in mammograms earlier than humans are presently able to, and whether or not machine learning can allow doctors to use all the large quantities of data available on patients to make more customized treatment decisions.

It’s a discipline some say is on the cusp of adjusting the way we use medications.

“The potential is probably the largest in any sort of expertise we have ever had within the discipline of medication,” mentioned Dr. Laureen Stein, director of the Bio-Informatics Technology Institute. “Computing functionality can transcend what a human being might ever accomplish of their lifetime.”

Funding is pouring in, from tech-laden Silicon Valley venture capitalists, tech giants like IBM’s Watson, Alphabet and Philips, to pharmaceutical corporations and swiftly proliferating startups. The marketplace for artificial intelligence in health care and the life sciences is projected to grow by 40 % a year, to $6.6 billion in 2021, in line with estimates from Frost & Sullivan.

Among the earliest applications are expected to be in diagnosing disease. For Odegard, the power of computer systems to scour through vast quantities of data holds the potential of earlier detections in diseases. “Humans can only process so much, specifically, our largest bottleneck is in our inherent IO (input/output) constraints.” Odegard said while demonstrating an early look at his machine AI effortlessly crunching through tens of millions of datasets as it learns from patterns and starts to write its own evaluation primarily based on the data findings.

While AI won’t be replacing medical doctors anytime soon, it’s going to provide physicians with instruments to more efficiently — and reliably — assess patients. AI is already involved in mining medical knowledge, diagnosing medical photos, learning genomics-based data for personalised drugs, and enhancing the lives of the disabled. Some medical analysis centers and startups are automating the evaluation of MRIs, CT scans, and X-rays to help physicians in making a diagnosis. Others are using deep learning to create genetic interpretation engines to determine cancer-causing mutations in patient genomes, bringing to life the concept of customized drug therapy.

Nevertheless, whereas AI will no doubt continue to revolutionize medicine for years to come, physicians typically find themselves perplexed by incorporating the technology into their regular practice. Only as soon as AI is accepted, and fully integrated into medicine will we see the complete potential for the technology by way of lending itself to more efficient and accurate diagnostics — from routine checkups to more specialised fields.

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