Table 1.

Definition of Artificial Intelligence and Its Subfields

Artificial intelligence (AI)	• An umbrella term that encompasses a vast degree of computer technologies (e.g., expert systems, computer vision, robotics, and machine learning) as well as the concept of a machine imitating human intelligence  • A system with the ability to perceive data, to employ and compare different algorithms to achieve specific goals, to analyze their performance and tune them, and then apply to unseen data, and repeat the previous process and update the previous learning
Expert system	• Subset of AI  • Rule-based systems built with explicit coding of decision rules
Machine learning	• Subset of AI  • Training a computer model to solve problems (e.g., prediction) by using statistical theories or identifying specific patterns in the data (e.g., phenotyping)
Deep learning	• Subset of machine learning  • Algorithms to process multiple layers of information to model intricate relationships among data
Decision tree	• Supervised machine learning algorithm  • Flowchart structure like a tree that has internal nodes, branches, and leaves   ○ Internal nodes contain questions such as whether a patient has a fever >100.4F   ○ Branches represent the answer (i.e., yes or no)   ○ Leaves represent final class labels  • Random forest is an ensemble of decision trees
K-nearest neighbor	• Supervised machine learning algorithm  • Is used for classification and regression tasks based on similarities (i.e., proximity or distance) between available data and new data
Naïve Bayes	• Supervised machine learning algorithm  • Probabilistic classifiers based on Bayes’ theorem with an assumption of independence among predictor variables
K-means clustering	• Unsupervised machine learning algorithm  • Identify similar characteristics in the dataset and partition into subgroups

AI; artificial intelligence