Artificial intelligence in perioperative medicine: a narrative review

Abstract

Recent advancements in artificial intelligence (AI) techniques have enabled the development of accurate prediction models using clinical big data. AI models for perioperative risk stratification, intraoperative event prediction, biosignal analyses, and intensive care medicine have been developed in the field of perioperative medicine. Some of these models have been validated using external datasets and randomized controlled trials. Once these models are implemented in electronic health record systems or software medical devices, they could help anesthesiologists improve clinical outcomes by accurately predicting complications and suggesting optimal treatment strategies in real-time. This review provides an overview of the AI techniques used in perioperative medicine and a summary of the studies that have been published using these techniques. Understanding these techniques will aid in their appropriate application in clinical practice.

Trends in Research on Artificial Intelligence in Anesthesia: A VOSviewer -Based Bibliometric Analysis

“…They presuppose that the clinician should acquire adequate baggage of statistical and IT knowledge; on the other hand, computer scientists must be able to adequately answer the clinician's research queries. This interdisciplinary collaboration process needs to be implemented as AI-based models must be clinically validated to confirm their postulated utility [6]. Since AI works by approaching big data, a key aspect of this translational pathway is the capability of computer scientists to work on well-structured datasets.…”

“…The application of AI methods toward clinical decision support has been demonstrated to be effective in many fields of medicine [3,4], including clinical anesthesia [5]. In particular, AI has been investigated for stratification of the risks of anesthesia and event prediction, depth of anesthesia monitoring, anesthesia management (e.g., closed-loop anesthesia), ultrasound-based procedures (nerve blocks and vascular access), pain management, operating room logistics, and other aims [6,7]. As Alexander et al [8] highlighted, in the field of anesthesia, AI can help in decision-making but can mainly enable anesthesiologists to address clinical issues through a proactive, rather than reactive, approach.…”

Artificial intelligence in surgery: the emergency surgeon’s perspective (the ARIES project)

“…For example, in oncology, research has demonstrated that ML applications can be of great help for the diagnosis or detection of cancer [5]. In the intensive care unit, with the implementation of the "internet of things" (IoT), which includes electronic systems used for communication between individuals with the final goal of enabling a variety of things/objects present in the environment to be connected and to interact and cooperate anytime, anyplace, with anything and anyone, the monitoring of the patient becomes "real-time" even remotely [6][7][8].…”

Real-time machine learning model to predict short-term mortality in critically ill patients: development and international validation

“…These models include a gradient boosting machine (GBM) model for in-hospital mortality [ 3 ], a recurrent neural network-based model for major complications, and a hybridized convolutional neural network and long short-term memory (LSTM) model for 3 to 14-day mortality [ 4 ]. Previous studies have reported the excellent performance of models, suggesting their potential use in clinical practice [ 5 ].…”