Table 1.

Definitions of terms related to AI.

Term	Definition
Artificial Intelligence (AI)	The first definition was been given in 1950 by Alan Turing, the founding father of AI, as the science and engineering of making intelligent machines, especially intelligent computer programs [3]. According to Salto-Tellez M. et al. [4], AI represents a range of advanced machine technologies that can derive meaning and understanding from extensive data inputs, in ways that mimic human capabilities. In the present context of medical practice, a specific definition may be a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation [5].
Machine Learning (ML)	ML, a subset of artificial intelligence, exhibits the experiential “learning” associated with human intelligence, while also having the capacity to learn and improve its analyses through the use of computational algorithms [6,7]. Alpaydin E. [8] defined machine learning as the field of programming computers to optimize a performance criterion using example data or past experience. ML-based tools are used in the healthcare system to provide various treatment alternatives and individualized treatments and improve the overall efficiency of hospitals and healthcare systems while lowering the cost of care [9].
Deep Learning (DL)	Deep Learning, a subset of Machine Learning, refers to a deep neural network, which is a specific configuration where neurons are organized in multiple successive layers that can independently learn representations of data and progressively extract complex features, performing tasks such as computer vision and natural language processing (NLP) [10]. In experimental settings across multiple medical specialties, DL performs equivalently to healthcare professionals for detecting diseases from medical imaging [11].
Natural Language Processing (NLP)	Natural Language Processing is a theoretically-motivated range of computational techniques for analyzing and representing naturally-occurring texts at one or more levels of linguistic analysis for the purpose of achieving human-like language processing for a range of tasks or applications [12]. NLP techniques have been used to structure information in healthcare systems by extracting relevant information from narrative texts so as to provide data for decision-making [13].
Robotics	The robot has been defined as “a reprogrammable multifunctional manipulator designed to move material, parts, tools, or specialized devices through variable programmed motions for the performance of a variety of tasks” by the Robot Institute of America [14]. The term “robotics” refers to the study and use of robots. Robotic assistance has been shown to improve the safety and performance of intracorporeal suturing, which is heavily required in urological and gynecological procedures [15].
Artificial Neural Network (ANN)	An Artificial Neural Network, a subset of Machine Learning, is a computational model inspired by the biological neural networks in the human brain. These systems are mainly used for pattern identification and processing and are able to progressively improve their performance based on analytic results from previous tasks [16,17,18]. Many areas have been integrating the use of ANNs to facilitate the diagnosis, prognosis, and treatment of many diseases [19,20,21].
Convolutional Neural Network (CNN)	A Convolutional Neural Network is a Deep Learning algorithm specifically designed for image and video processing, primarily used in medical image analysis and diagnostics. CNNs have demonstrated superior performance as compared with classical machine learning algorithms and in some cases achieved comparable or better performance than clinical experts [22].