Artificial Intelligence-related Literature in Transplantation: A Practical Guide

摘要

INTRODUCTION Since John McCarthy introduced the term “artificial intelligence (AI)” in 1955,1 AI research has been growing. “AI” is an umbrella term that encompasses a vast degree of computer technologies (eg, expert systems, computer vision, robotics, and machine learning) (Figure 1A) as well as a concept of a machine-imitating human intelligence2,3 (Table 1). Modern AI is defined as a system’s ability to (1) perceive the current world, ie, data; (2) to cause and compare different approaches to achieve specific goals based on given data; (3) to tune their performance and apply to unseen data; and (4) to repeat the previous processes multiple times and update the previous learning.4 When reviewing results from AI models, it is therefore critical to understand whether they are appropriately developed and validated (Figure 1B). TABLE 1. - Definition of artificial intelligence and its subfields AI □ An umbrella term that encompasses a vast degree of computer technologies (eg, 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 cause 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 (eg, prediction) by using statistical theories or identifying specific patterns in the data (eg, 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 (ie, 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 (ie, 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. FIGURE 1.: History of artificial intelligence and methodological evolution of artificial intelligence-related literature. A, A brief history of artificial intelligence: artificial intelligence was introduced in the 1950s along with machine learning.1 , 3 Since the 1970s, expert systems showed some success, such as in electrocardiogram interpretation.3 In the 2010s, substantial advances were achieved with deep learning, including image classification.3 B, Methodological evaluation of artificial intelligence algorithms. C, Area under the receiver operating characteristic. AUROC, area under the receiver operating characteristic.The Quality and Quantity of Input Data Like conventional parametric and semiparametric diagnostic or prognostic models, the quality of the conclusions drawn from the AI-based algorithms relies on the characteristics of the dataset, which is used to train the model. If the model is trained on/based on a biased dataset, the model itself is likely to be biased.5 AI might assume that probabilities are static or outcomes within the dataset are optimized, which might not be the case. Hence, similar to conventional methods, it is essential to look at the exclusion and inclusion criteria of the study. For example, a model for postliver transplant (LT) graft survival, which focuses on matching LT donor an