Augmented Intelligence and Nursing.
Augmented Intelligence and Nursing.
复制标题
增强智能和护理。
DOI：
10.1097/01.nep.0000000000000124
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发表时间：
2017
影响因子：
1
通讯作者：
D. Skiba
中科院分区：
文献类型：
--
作者：
D. Skiba
随着每一个新的一年，许多报告，专栏和博客被写来预测医疗保健领域即将到来的技术和趋势。2017年的几个技术趋势表明，人工智能（AI）在医疗保健中的使用越来越多。Gartner的2017年十大战略技术趋势（Cearley，步行者，& Burke，2016）突出了三大趋势：A和高级机器学习，智能应用程序和智能事物。首先，某些技术和特定技术，如深度学习、神经网络和自然语言处理，都包含在人工智能和机器学习概念中。这些技术创建的软件程序不仅仅是基于规则的系统。相反，这些系统可以“理解、学习、预测、适应并显得智能”（Cearley et al.，2016）。他们的学习能力是其功能的关键。例如，机器学习系统可以分析大量电子健康记录（EHR）并推荐潜在的有效治疗方法。随着更多数据集的添加，系统可以学习和调整建议，例如，将基因组数据添加到EHR数据库。智能应用程序，第二个趋势，指的是虚拟个人助理（VPA）。VPA帮助用户处理日常任务，例如，分类电子邮件或回答简单的问题（就像Siri™和Cortana™在我们的智能手机上一样）。VPA将在未来一年更多地用于医疗保健。第三个趋势智能物联网分为三个不同的类别：机器人、无人机和自动驾驶汽车。在讨论2017年健康信息技术趋势时，Health Data Management（2016）也将人工智能列为第一趋势，并指出尽管人工智能在2016年在医疗保健领域呈爆炸式增长，但应用程序通常非常专业。预计会有更广泛的使用，这“将意味着更好地获得可操作的情报。Padmanabhan（2016）回应了这一观点：“在2017年，我们更有可能听到‘认知计算’和‘人工智能’等术语，而不太可能听到‘大数据分析’这个术语，现在它似乎限制了对高级分析中实际工作的描述。“人工智能基础人工智能这个术语并不新鲜。它可以追溯到20世纪40年代和50年代，图灵（1950）问机器是否可以思考。人工智能甚至在20世纪70年代被用于医疗保健，例如，斯坦福大学开发的一个名为MYCIN的系统，可以识别细菌感染并推荐治疗方法（Shortliffe，1976）。MYCIN包含三个组成部分：由专家创建的知识库、具有基于规则的算法的推理机和用户界面。尽管在那个时代设计了其他专家系统，但从未有过大量的用户将其用于临床实践。大多数医疗保健专业人员认为没有必要用机器来告诉他们如何练习。情况已经改变了。根据总统执行办公室，国家科学技术理事会技术委员会（2016年，第6页），人工智能目前的动机是“由三个相互加强的因素驱动：大数据的可用性。·极大地改进了机器学习方法和算法，以及更强大的计算机的能力。认知计算（CC）是一个新兴的术语，有些人认为它是人工智能的一个子集。Kelly（2016年，第1页）认为，未来的技术将是“认知的，而不是人工的”，并将CC定义为“大规模学习的系统，有目的地推理，自然地与人类互动。另一个区别因素是CC可以处理“非结构化数据”，而AI应用程序通常基于结构化或数字数据。马尔（2016）将CC总结为“认知科学（研究人类大脑及其功能）和计算机科学的混搭，其结果将对我们的私人生活、医疗保健、商业等产生深远的影响。…
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™ and Cortana™ 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. …