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. 2023 Oct 3;228(Suppl 4):S322–S336. doi: 10.1093/infdis/jiad158

Table 1.

Definitions of Key Terms

Term Definition
AI Simulation of human intelligence in machines
Artificial neural networks Algorithm inspired by biological neural networks in which interconnected neurons process information
Convolutional neural networks Type of neural network that uses a series of learnable convolutional layers to distill spatial features from imaging data
Deep learning A subset of ML, algorithms that use an artificial neural network to extract high-level features from data; these methods can be used to distill complex data types, such as images and text for predictive tasks
Labeled data Data that include class labels; for example, if the task is to predict the fruit name (class label) given the color and shape, the labeled data would include the fruit name, color, and shape
Low/poor-quality labeled data A labeled data set wherein the label is not accurate for some data points
ML A subset of AI, algorithms that learn without explicit instructions
Model generalizability The ability for a model to perform well on new data it has not been trained on
Partially labeled data A data set that includes some combination of labeled and unlabeled data
Preclinical model Nonhuman (typically animal) model of a disease in humans
Self-supervised learning A type of ML in which the algorithm learns from unlabeled data to form representations; the representations can be used later to better complete a more useful downstream task
Supervised learning A type of ML in which the algorithm learns from labeled data to produce the label for new data
Traditional/classic ML A subset of ML, algorithms that learn from structured (tabular) data
Unlabeled data Data that do not include class labels; for example, if the task is to predict the fruit name (class label) given the color and shape, the unlabeled data would include only the fruit color and shape
Weakly supervised or semi-supervised learning A type of ML that falls between self-supervised and supervised learning, in which a small amount of labeled and a large amount of unlabeled data are used for model training

Abbreviations: AI, artificial intelligence; ML, machine learning.