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Thanks largely in part to Hollywood and science fiction, AI is synonymous in people's minds with walking, talking robots. However, AI expands beyond robotics into machine learning and natural language processing, all of which can find applications in the healthcare field [2]. Care robots or “carebots” do exist, but they are far from the androids that appear in Westworld. There are several schools of thought of how to classify AI in healthcare. One perspective views AI being put into three categories: diagnosis, clinical decision making, and personalized machines [2]. In comparison, another school of thought believes there are two main categories with subcategories in each. In this viewpoint, the main categories are virtual and physical [3].

Before being able to define AI's role, it is important to understand the capabilities of AI. Using ML, AI is able to process large amounts of data and look for patterns, including patterns that are often missed or overlooked by humans. For many, this pattern identification is used as a secondary consult to confirm a doctor's diagnosis [2]. There is an inherent trust in these AI and ML by healthcare professionals (HCPs). HCPs are often overworked and understaffed, and so using the AI to confirm diagnosis means that they are assuming the following: (i) the machines were coded correctly and tested so that they will identify the patterns correctly and perform as expected, (ii) those who performed the coding have at least some understanding of the healthcare, and (iii) the machines have not been tampered with. Later in this chapter, the third point of trust will be addressed, whether this trust is wrongfully placed or not. This chapter does not investigate the manufacturing of these machines, so for the intent of this topic, it will be assumed that the first point of trust is valid. Looking at the second point of trust, though, does bring up an obvious issue. For many, practicing medicine is a lifelong journey of continual education, and as things are now, most doctors do not know how to code at the level of creating AI. This means that HCPs and programmers must work together to identify trends, patterns, and known symptomatic association that the AI would use. This limitation is likely why AI is kept in the passenger seat and used as a secondary diagnosis, rather than replacing HCPs as the primary diagnostician.