Artificial intelligence tools permeate the health care landscape, even though many health care practitioners don't realize that they're leveraging such tools in their everyday practice. The American Medical Association, in a report from its 2018 Annual Meeting, described AI as: "a host of computational methods that produce systems that perform tasks normally requiring human intelligence. These computational methods include, but are not limited to, machine image recognition, natural language processing, and machine learning." The AMA emphasized in its report, however, that another term used in the health care setting with regard to AI is "augmented intelligence," given that AI generally is designed to "enhance the capabilities of human clinical decision making," particularly in the health care industry. In other words, AI is a tool that, at its best, helps humans make better decisions, and complete tasks more efficiently and effectively.

Machine learning is becoming more prevalent than ever before and is slowly integrating into our day–to–day lives. From maximizing efficiency in the workplace to better understanding how consumers emotionally connect to brands, services and products, the applications of artificial intelligence seem limitless. But some industries have been quicker than others to adopt this frontier technology. Since major shifts can be disruptive, planning ahead can help avoid some of the growing pains. That's why I asked members of the Young Entrepreneur Council: The analysis is supposed to be what humans bring to data, but programs are getting better and better at identifying trends before a human would have noticed them.

Machine learning is becoming more prevalent than ever before and is slowly integrating into our day–to–day lives. From maximizing efficiency in the workplace to better understanding how consumers emotionally connect to brands, services and products, the applications of artificial intelligence seem limitless. But some industries have been quicker than others to adopt this frontier technology. Since major shifts can be disruptive, planning ahead can help avoid some of the growing pains. That's why I asked members of the Young Entrepreneur Council: The analysis is supposed to be what humans bring to data, but programs are getting better and better at identifying trends before a human would have noticed them.

Machine learning is becoming more prevalent than ever before and is slowly integrating into our day–to–day lives. From maximizing efficiency in the workplace to better understanding how consumers emotionally connect to brands, services and products, the applications of artificial intelligence seem limitless. But some industries have been quicker than others to adopt this frontier technology. Since major shifts can be disruptive, planning ahead can help avoid some of the growing pains. That's why I asked members of the Young Entrepreneur Council: The analysis is supposed to be what humans bring to data, but programs are getting better and better at identifying trends before a human would have noticed them.

Machine learning is becoming more prevalent than ever before and is slowly integrating into our day–to–day lives. From maximizing efficiency in the workplace to better understanding how consumers emotionally connect to brands, services and products, the applications of artificial intelligence seem limitless. But some industries have been quicker than others to adopt this frontier technology. Since major shifts can be disruptive, planning ahead can help avoid some of the growing pains. That's why I asked members of the Young Entrepreneur Council: The analysis is supposed to be what humans bring to data, but programs are getting better and better at identifying trends before a human would have noticed them.