From the May 2017 issue of HealthCare Business News magazine
By Bipin Thomas
Artificial intelligence (AI) is defined as the science and engineering of creating intelligent computer systems that are capable of performing tasks without receiving instructions directly from humans.
These computer systems use a number of different algorithms and decision-making capabilities, as well as vast amounts of data, to provide a solution or response to a request. The applications of AI have been proliferating at a much faster pace across all industries, including health care.
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The health care startups entering the artificial intelligence space increased in recent years. Health care deals focused on AI startups went up from less than 20 in 2012 to nearly 70 in 2016. Today, there are hundreds of companies that are applying machine learning algorithms and predictive analytics to reduce drug discovery times, provide virtual assistance to patients and diagnose ailments by processing medical images, among other things. By 2020, AI systems will be involved in everything from population health management to digital avatars capable of interacting with patients over video.
IBM’s Watson is the most popular AI-driven system that has been tackling cancer diagnostics on par with human physicians. Google’s DeepMind is revolutionizing eye care in the U.K. Both of these AI systems use deep learning, a concept loosely mirroring how our own brains work by having AI software analyze exorbitant amounts of data and uncover patterns — which is particularly applicable in diagnostics. Whenever a new technology enters health care, there are a number of challenges it faces. Common setbacks of AI in health care include a lack of electronic data exchange, regulatory compliance requirements and patient and provider adoption. AI has come across all of these issues, narrowing down the areas in which it can succeed.
Artificial intelligence is making its way into the realm of modern health care. While AI won’t be replacing physicians, it will provide doctors with intelligent tools to assess patients more efficiently and reliably. AI is already involved in mining medical data, diagnosing medical images and analyzing genomics-based data for personalized medicine. Hospitals can now efficiently compare a single patient’s tests and history with data from a vast population of other patients. There is tremendous progress in automating the analysis of MRIs, CT scans and X-rays to assist physicians in making a diagnosis. Others are utilizing deep learning to create genetic interpretation engines to identify cancer-causing mutations in patient genomes, bringing to life the concept of personalized medicine.