Artificial Intelligence in Healthcare: Our Next Great Innovation

Artificial Intelligence in Healthcare: Our Next Great Innovation Posted By:

In 1935, the great British mathematician Alan Turing described a math machine that had limitless capabilities of solving highly complex equations and would think for itself.1 Many saw the early workings of a similar machine in the hit movie “The Imitation Game,” in which Turing developed a thinking code breaker during World War II.2 The Turing test was the early birth of what we know as artificial intelligence (AI), and our world is being consumed with this technology and innovation in every direction we turn. In healthcare, AI and its use are in its infancy. Every direction we turn in this industry has the potential for AI to be used, analyzed, and improved for every function we do. In this multipart series of articles, I work to break down what this innovation is all about and how physician associates and nurse practitioners will use it in everyday practice.

AI and Common Terminology
In its plainest language, AI is the science of a machine that can imitate human behavior.3 The ability of AI has far-reaching implications throughout our world. In healthcare, AI could be used in various fashions, from predicting a prognosis and diagnosis to learning greater efficiency within a healthcare system. To have a better understanding of AI, it is essential to grasp the many different terms that are associated with this technology. To illustrate the complexity of AI, it is first essential to grasp the different terminology used in the science. The table presents a short list of standard terms used when discussing this complex subject. Phrases such as “deep learning,” “generative AI,” “large language model,” and “neural network” are just a few terms used in the literature to describe the many uses of AI.

As technology advances with AI in healthcare, it will be vital for all practitioners to understand the basic terms of this new science.

Table 1. AI Common Terminology3-5

AI and Data Collection Models
The digital information age has transformed the way we operate in healthcare. To paraphrase a 2010 quote from Eric Schmidt, cofounder of Google, we now create as much information every 2 days as humans did from the dawn of civilization to the year 2003.6 This statement profoundly illustrates that the enormous amount of data produced each day far exceeds what the human mind can comprehend. Because of this, the amount of healthcare data we possess within our current system is enormous. It would benefit the healthcare system to harness these data so we can intelligently evaluate processes that can improve healthcare and efficiency in its delivery. To do this, the first fundamental component that AI must possess is accurate data that are placed within a system to allow it to be processed properly. This is especially true as we consider how research experiments and data analytics are conducted daily.7

However, although AI is invaluable for processing vast amounts of data, there can be drawbacks to a system that generates AI data analytics. The output data are produced on basic assumptions that all input data are accurate. We know this is not the case, as statistical outliers and confounders can cause data analytics to exclude certain populations or health conditions that would have otherwise been considered. 7 Because of these constraints, we must ensure that the most accurate data and prediction models are used so that AI can generate associations that accurately describe what is happening in healthcare. In addition, the ability to generalize our data and create reproducible results could be jeopardized if the original data are inaccurate.

AI and Medicine: Utilization of Technology in Healthcare
In this first section of this AI in healthcare article, we defined AI and some of the common terms used in the science. We also broadly described data collection and acknowledged how research can only be conducted if the data input into the system are accurate. The second submission of this series will discuss AI and its use in technology and healthcare. How this technology and innovation affects physician associates and nurse practitioners will also be explored. Finally, in third article, we will discuss the overall impact of AI and its utility in healthcare and consider its limitations.


  1. Britannica. Alan Turing and the beginning of AI. Accessed February 27, 2024.
  2. IMDb. The Imitation Game. Accessed February 27, 2024
  3. Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol. 2019;28:73-81.
  4. Sahni NR, Carrus B. Artificial intelligence in U.S. health care delivery. N Engl J Med. 2023;389:348-358.
  5. Chen M, Decary M. Artificial intelligence in healthcare: an essential guide for health leaders. Healthc Manage Forum. 2020;33:10-18.
  6. Schmidt E. Panel Discussion at Techonomy 2010. Presented at: Techonomy; 2010.
  7. Hunter DJ, Holmes C. Where medical statistics meets artificial intelligence. N Engl J Med. 2023;389:1211-1219.


Filed under: Health Policy and Trends , Miscellaneous , NPs & PAs

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