Evidence map›Paper›PMID 39232648›Full record

SynthesisBMC anesthesiology2024

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

Kensuke Shimada, Ryota Inokuchi, Tomohiro Ohigashi, Masao Iwagami, Makoto Tanaka, Masahiko Gosho, Nanako Tamiya

Abstract readSystematic ReviewMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in BMC anesthesiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Review
  6. Review
  7. Article
  8. Observational
  9. Article
  10. Review
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  12. Article
  13. Reshaping Anesthesia with Artificial Intelligence: From Concept to Reality.Anesthesia and critical care (Houston, Tex.) · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Kensuke ShimadaGraduate School of Comprehensive Human Sciences, University of Tsukuba, Ibaraki, Japan.
Ryota InokuchiDepartment of Health Services Research, Institute of Medicine, University of Tsukuba, Ibaraki, Japan. inokuchir-icu@h.u-tokyo.ac.jp.
Tomohiro OhigashiDepartment of Information and Computer Technology, Faculty of Engineering, Tokyo University of Science, Tokyo, Japan.
Masao IwagamiDepartment of Health Services Research, Institute of Medicine, University of Tsukuba, Ibaraki, Japan.
Makoto TanakaDepartment of Anesthesiology, Institute of Medicine, University of Tsukuba, Ibaraki, Japan.
Masahiko GoshoDepartment of Biostatistics, Institute of Medicine, University of Tsukuba, Ibaraki, Japan.
Nanako TamiyaDepartment of Health Services Research, Institute of Medicine, University of Tsukuba, Ibaraki, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIntegration 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).

methodsWe 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.

resultsEight 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, I

conclusionsThis 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 registrationThis study was registered with the International Prospective Register of Systematic Reviews (PROSPERO ID: CRD42022353727).

Indexed as

AnesthesiaArtificial IntelligenceHumansPerioperative CareRandomized Controlled Trials as TopicAnesthesiaArtificial intelligenceMeta-analysisRondomized controlled trialSystematic review

Identifiers

PMID39232648
PMCPMC11373311

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.