Table 1.

Major Subdisciplines of Artificial Intelligence

Subdiscipline	Description	Examples	References
Machine learning	Algorithms able to uncover associations in large data sets via pattern recognition among interacting variables. Subcategories include supervised and unsupervised learning.	• Supervised learning: An application tested with photographs to monitor postoperative free flap viability based on skin color.	Noorbakhsh-Sabet et al5; Bogle et al.6; Ebert and Golub7; Knoops et al.8
		• Unsupervised learning: The organization and interpretation of large amounts of unlabeled genetic data without a training set.
Deep learning	Machine learning models that use artificial neural networks to improve predictive performance and accuracy with continued training.	• A deep learning convolutional network to determine rhinoplasty status via photographs.	Borsting et al9; Phillips et al10,11
		• An application capable of identifying melanoma in images of biopsied lesions taken via a smart phone.
Natural language processing	Machine learning software capable of understanding, interpreting, and manipulating human language.	• An AI bot within a smartphone application capable of providing answers to frequently asked questions among preoperative patients.	Mehta and Devarakonda12; Savova et al13; Jokhio et al14; Chopan et al15; Dodds et al16
Facial recognition	AI software capable of recognizing human faces by using biometrics to map facial features and compare the data with a database of photographs.	• Facial recognition neural networks capable of gender-typing transgender women after facial feminization surgery.	Zuo et al17; Chen et al18