Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Meta-Analysis
. 2024 Sep 4;24(1):306.
doi: 10.1186/s12871-024-02699-z.

Artificial intelligence-assisted interventions for perioperative anesthetic management: a systematic review and meta-analysis

Kensuke Shimada  1   2   3 , Ryota Inokuchi  4   5   6 , Tomohiro Ohigashi  7   8 , Masao Iwagami  9 , Makoto Tanaka  10 , Masahiko Gosho  8 , Nanako Tamiya  9   11   12   13
Affiliations
Meta-Analysis

Artificial intelligence-assisted interventions for perioperative anesthetic management: a systematic review and meta-analysis

Kensuke Shimada et al. BMC Anesthesiol. .

Abstract

Background: Integration of artificial intelligence (AI) into medical practice has increased recently. Numerous AI models have been developed in the field of anesthesiology; however, their use in clinical settings remains limited. This study aimed to identify the gap between AI research and its implementation in anesthesiology via a systematic review of randomized controlled trials with meta-analysis (CRD42022353727).

Methods: We searched the databases of Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica Database (Embase), Web of Science, Cochrane Central Register of Controlled Trials (CENTRAL), Institute of Electrical and Electronics Engineers Xplore (IEEE), and Google Scholar and retrieved randomized controlled trials comparing conventional and AI-assisted anesthetic management published between the date of inception of the database and August 31, 2023.

Results: Eight randomized controlled trials were included in this systematic review (n = 568 patients), including 286 and 282 patients who underwent anesthetic management with and without AI-assisted interventions, respectively. AI-assisted interventions used in the studies included fuzzy logic control for gas concentrations (one study) and the Hypotension Prediction Index (seven studies; adding only one indicator). Seven studies had small sample sizes (n = 30 to 68, except for the largest), and meta-analysis including the study with the largest sample size (n = 213) showed no difference in a hypotension-related outcome (mean difference of the time-weighted average of the area under the threshold 0.22, 95% confidence interval -0.03 to 0.48, P = 0.215, I2 93.8%).

Conclusions: This systematic review and meta-analysis revealed that randomized controlled trials on AI-assisted interventions in anesthesiology are in their infancy, and approaches that take into account complex clinical practice should be investigated in the future.

Trial registration: This study was registered with the International Prospective Register of Systematic Reviews (PROSPERO ID: CRD42022353727).

Keywords: Anesthesia; Artificial intelligence; Meta-analysis; Rondomized controlled trial; Systematic review.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Flow chart of the study selection process. Abbreviations: CENTRAL, Cochrane Central Register of Controlled Trials; Embase, Excerpta Medica Database; IEEE, Institute of Electrical and Electronics Engineers Xplore; MEDLINE, Medical Literature Analysis and Retrieval System Online
Fig. 2
Fig. 2
Forest plot and funnel plot of meta-analyses of each outcome. Abbreviations: AI, artificial intelligence; HPI, Hypotension Prediction Index. a, b Forest plot and funnel plot of meta-analysis of intraoperative time-weighted average of the area under the threshold with vs without the Hypotension Prediction Index. c, d Forest plot and funnel plot of meta-analysis of duration of intraoperative hypotension with vs without the Hypotension Prediction Index
Fig. 3
Fig. 3
Risk of bias. A Summary plot of risk of bias. B Risk of bias of each study
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56. 10.1038/s41591-018-0300-7 - DOI - PubMed
    1. Palla K, Hyland SL, Posner K, Ghosh P, Nair B, Bristow M, Paleva Y, Williams B, Fong C, Van Cleve W, et al. Intraoperative prediction of postanaesthesia care unit hypotension. Br J Anaesth. 2022;128(4):623–35. 10.1016/j.bja.2021.10.052 - DOI - PMC - PubMed
    1. Xue B, Li D, Lu C, King CR, Wildes T, Avidan MS, Kannampallil T, Abraham J. Use of machine learning to develop and evaluate models using preoperative and intraoperative data to identify risks of postoperative complications. JAMA Netw Open. 2021;4(3):e212240. 10.1001/jamanetworkopen.2021.2240 - DOI - PMC - PubMed
    1. Nolde JM, Schlaich MP, Sessler DI, Mian A, Corcoran TB, Chow CK, Chan MTV, Borges FK, McGillion MH, Myles PS, et al. Machine learning to predict myocardial injury and death after non-cardiac surgery. Anaesthesia. 2023;78(7):853–60. 10.1111/anae.16024 - DOI - PubMed
    1. Lee CK, Hofer I, Gabel E, Baldi P, Cannesson M. Development and validation of a deep neural network model for prediction of postoperative in-hospital mortality. Anesthesiology. 2018;129(4):649–62. 10.1097/ALN.0000000000002186 - DOI - PMC - PubMed

LinkOut - more resources