**1. Introduction**

AI has been rapidly increasing in popularity and application within the healthcare industry over the past decade. AI has the potential to greatly increase the efficiency and accuracy of healthcare, resulting in improved patient care, better decisionmaking, and overall cost savings. AI allows for complex and rapidly growing datasets to be evaluated and analyzed with unprecedented accuracy and detail, using machine learning (ML), neural networks (NNs), deep learning (DL), and large language models (LLM).

AI and ML are two distinct but related branches of computer science. The two are related in that AI requires data to be used in order to make decisions, and ML provides the tools to do so. Artificial intelligence is a spectrum of intelligence, learning, and analytical proficiency. Machine learning and deep learning are related applications within the artificial intelligence space with varying aptitudes and capabilities (see **Figure 1**). Machine learning (ML) can understand relationships from the data without the need to define them a priori and can derive predictive models without a need for strong assumptions about the underlying mechanisms. In other words, ML converts the inputs of an algorithm into outputs, using statistical tools [1, 2]. It can change when exposed to new data and can improve from experience.

In contrast, deep learning (DL) uses multilayered neural networks to compute large volumes of data and accept multiple data types (heterogenicity). This feature has proven applicability in healthcare, that is. the EHR system. Of the deep learning algorithm, convolutional neural networks (CNN) processes data exhibiting natural spatial invariance (clinical images) [1]. Compared to ML, DL requires considerably less human guidance, and the overall difference is how DL interprets and presents raw data.

The level of analysis, sophistication, and detail exhibited by AI would be impossible for humans to do alone. This can help healthcare organizations to gain insights and identify trends that would otherwise be difficult to detect. In addition, AI can provide real-time recommendations and feedback to healthcare professionals, helping them make better, more informed decisions.

As AI technology advances and becomes more integrated into healthcare systems, healthcare organizations can leverage its many advantages to become more efficient and effective such as automating mundane and repetitive tasks freeing up healthcare professionals to concentrate on more critical aspects of patient care. AI can also improve the accuracy and timeliness of diagnosis and treatment decisions, reducing the risk of medical errors, and potentially saving lives. Additionally, AI can be used to monitor patient health, alert healthcare professionals to potential issues before they become serious, predict future health outcomes, and help healthcare organizations

**Figure 1.**

*Artificial intelligence (AI) hierarchical relationship.*
