Augmented Intelligence and Nursing.
Venue: Nursing education perspectives
Year: 2017

With each new year, numerous reports, columns, and blogs are written to project the upcoming technologies and trends in health care. Several 2017 technology trends point to the growing use of artificial intelligence (AI) in health care. Gartner's Top 10 Strategic Technology Trends for 2017 (Cearley, Walker, Burke, 2016) highlight three top trends: A and advanced machine learning, intelligent apps, and intelligence things. Let's look at each.First, certain technologies and specific techniques, such as deep learning, neural networks, and natural language processing, are encompassed within the AI and machine learning concept. These techniques create software programs that are more than just rulebased systems. Rather, these systems can "understand, learn, predict, adapt and appear intelligent" (Cearley et al. 2016) Their ability to learn is key to their functionality. For example, a machine learning system can analyze numerous electronic health records (EHRs) and recommend potential effective treatments. As more datasets are added, the system can learn and adapt the recommendations, for example, adding genomic data to the EHR database.Intelligent apps, the second trend, refer to virtual personal assistants (VPAs) VPAs help users with everyday tasks, for example, sorting email or answering simple questions (just as Siri(tm) and Cortana(tm) do on our smartphones) VPAs will become more available in health care in the coming year. Intelligent things, the third trend, break down into three distinct categories: robots, drones, and autonomous vehicles.In discussing health information technology trends for 2017, Health Data Management (2016) also named AI as the first trend, stating that, although AI exploded in health care in 2016, applications were typically very specialized. More general use is projected, which "will mean better access to actionable intelligence." Padmanabhan (2016) echoed this notion: "In 2017, we are more likely to hear terms such as 'cognitive computing' and 'artificial intelligence' .and less likely to hear the term 'big data analytics, which now seems to be limiting in its description of the actual work being done in advanced analytics."AI BASICSThe term artificial intelligence is not new. It dates back to the 1940s and 1950s, and Turing (1950) asked if machines can think. AI was even used in health care in the 1970s, for example, with a system called MYCIN, developed by Stanford University, that identified bacterial infections and recommended treatments (Shortliffe, 1976) MYCIN contained three components: a knowledge base created by experts, an inference engine with rule based algorithms, and a user interface. Although there were other instances of expert system designed in that era, there was never a critical mass of users to adopt them for clinical practice. Most health care professionals did not see the need for machines to tell them how to practice.Circumstances have changed. According to the Executive Office of the President, National Science and Technology Council Committee on Technology (2016, p. 6) the current motivation for AI was "driven by three mutually reinforcing factors: the availability of big data. .dramatically improved machine learning approaches and algorithms ,and the capabilities of more powerful computers."COGNTIVE COMPUTINGCognitive computing (CC) is an emerging term that some view as a subset of AI. Kelly (2016, p. 1) believes that the future oftechnology will be "cognitive and not artificial" and defines CC in terms of "systems that learn at scale, reason with purpose and interact with humans naturally." One other distinguishing factor is that CC can handle "unstructured data," whereas AI applications are typically based on structured or numeric data. Marr (2016) summarizes CC as "a mashup of cognitive science the study of the human brain and how it functions and computer science, and the results will have far reaching impacts on our private lives, healthcare, business, and more.