Understanding basic principles of Artificial Intelligence: a practical guide for intensivists

Abstract

Background and aim: Artificial intelligence was born to allow computers to learn and control their environment, trying to imitate the human brain structure by simulating its biological evolution. Artificial intelligence makes it possible to analyze large amounts of data (big data) in real-time, providing forecasts that can support the clinician's decisions. This scenario can include diagnosis, prognosis, and treatment in anesthesiology, intensive care medicine, and pain medicine. Machine Learning is a subcategory of AI. It is based on algorithms trained for decisions making that automatically learn and recognize patterns from data. This article aims to offer an overview of the potential application of AI in anesthesiology and analyzes the operating principles of machine learning Every Machine Learning pathway starts from task definition and ends in model application.

Conclusions: High-performance characteristics and strict quality controls are needed during its progress. During this process, different measures can be identified (pre-processing, exploratory data analysis, model selection, model processing and evaluation). For inexperienced operators, the process can be facilitated by ad hoc tools for data engineering, machine learning, and analytics.

Figure 1.

Timeline diagram showing the history of artificial intelligence.

Figure 2.

Diagram representing the relationships between Artificial Intelligence, Machine Learning, and Deep Learning.

Figure 3.

Schematic Machine Learning pathway. Abbreviation: SVM, Support Vector Machine; RVM, Relevance Vector Machine; K-NN, K-nearest neighbors.

Figure 4.

Two examples of Model Selection for artificial neural networks in medical investigations. Random Forest can help the screening of the covariates for the classification model (A). Factor analysis can be adopted to select macro variables (B).

Figure 5.

Model fitting errors. Variance is the variability (distance from the target center) of the model prediction for a single point. Bias is the distance between expected (target center) and means values. In overfitting, the model has high Variance and low Bias. It shows high performance on the training set but not on the test one. Underfitting is characterized by high Bias and low Variance and produces a poor performance on the training set.