By Tom Sullivan.
Published in Healthcare IT News.
A look back at what happened and how it shaped the future of AI, cognitive computing and machine learning for 2018 and beyond.
Several things became clear this year: AI is real and it’s here with 86 percent of hospitals using some form of it and others like New York-Presbyterian already embarking on significant projects, there are two types of machine learning to understand now (those being supervised and unsupervised), and machine learning engineers are among the hottest emerging careers, and early practical applications include claims collection, clinical decision support, cybersecurity and radiology, just to name a few.
AI this year also sparked questions about ethics and emotional intelligence, notably that hospitals and companies outside healthcare need to create standards, obligations and metrics before deploying the technologies. And another. If a machine learning algorithm can be proven more effective than humans at reading radiological images will it be unethical to continue letting people do that job? That remains to be seen, of course, and it will likely be among the easier questions to answer in the long haul . . .