Artificial intelligence and anesthesia: a narrative review

Abstract

Background and Objective The aim of this narrative review is to analyze whether or not artificial intelligence (AI) and its subsets are implemented in current clinical anesthetic practice, and to describe the current state of the research in the field. AI is a general term which refers to all the techniques that enable computers to mimic human intelligence. AI is based on algorithms that gives machines the ability to reason and perform functions such as problem-solving, object and word recognition, inference of world states, and decision-making. It includes machine learning (ML) and deep learning (DL). Methods We performed a narrative review of the literature on Scopus, PubMed and Cochrane databases. The research string comprised various combinations of “artificial intelligence”, “machine learning”, “anesthesia”, “anesthesiology”. The databases were searched independently by two authors. A third reviewer would mediate any disagreement the results of the two screeners. Key Content and Findings The application of AI has shown excellent results in both anesthesia and in operating room (OR) management. In each phase of the perioperative process, pre-, intra- and postoperative ones, it is able to perform different and specific tasks, using various techniques. Conclusions Thanks to the use of these new technologies, even anesthesia, as it is happening for other disciplines, is going through a real revolution, called Anesthesia 4.0. However, AI is not free from limitations and open issues. Unfortunately, the models created, provided they have excellent performance, have not yet entered daily practice. Clinical impact analyzes and external validations are needed before this happens. Therefore, qualitative research will be needed to better understand the ethical, cultural, and societal implications of integrating AI into clinical workflows.

Artificial intelligence may enhance emergency triage and management

“…However, only a few randomized controlled trials have been conducted to evaluate the effectiveness of intranasal ketamine in emergency rooms or prehospital settings. 3,4 For example, Shimonovich et al 3 showed similar clinical efficacy for intranasal ketamine compared with intravenous and intramuscular morphine. In addition, the PAIN-K study demonstrated the efficacy of intranasal ketamine to control acute pain in civilian prehospital settings.…”

“…In addition, the PAIN-K study demonstrated the efficacy of intranasal ketamine to control acute pain in civilian prehospital settings. 4 Third, Dubecq et al reported the use of intranasal ketamine for pain management in a case series of combat casualties in the Middle East, which revealed reasonable results and no significant side effects (article in press). As part of the deployment, medical teams from the French Military Medical Service were deployed for four-month rotations from August 2017 to March 2019 and treated 259 casualties.…”

“…Technological precision and the ML system may increasingly guarantee an appropriate choice by the doctor, who is thus assisted in his diagnostic and therapeutic pathways. Artificial intelligence is based on algorithms that give to machines the ability to analyze big amount of data and perform functions such as problem-solving, object and word recognition, inference of world states, and decision making 4 . Moreover, machines have the possibility to learn from previous analysis and perfectionate the results during the process progressing.…”

Decision-making in anesthesiology: will artificial intelligence make intraoperative care safer?

“…To be most effective, artificial intelligence tools must obey the five rights of decision support: Providing the right information, to the right person, in the right format, through the right channel, at the right time in the workflow [39]. The algorithm's output is highly related to the quality of input, thus affecting the clinical decision support [15]. Furthermore, anesthesiologists will want the ability to understand why artificial intelligence has made a particular decision or recommendation.…”

“…An artificial intelligence-provided differential diagnosis may lead to treatment delays while the clinician awaits diagnostic test results to rule out proposed alternative diagnoses. There is limited research on what information will be needed and how it needs to be presented by artificial intelligence tools to support (and not hinder) decision-making and clinical workflows [15,42 ▪ ].…”