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

• Published in: BMC anesthesiology Vol. 24; no. 1; pp. 306 - 11
• Main Authors: Shimada, Kensuke, Inokuchi, Ryota, Ohigashi, Tomohiro, Iwagami, Masao, Tanaka, Makoto, Gosho, Masahiko, Tamiya, Nanako
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 04.09.2024; BioMed Central; BMC
• Subjects: Analysis; Anesthesia; Anesthesia - methods; Anesthesiology; Artificial Intelligence; Bias; Clinical trials; Fuzzy logic; General anesthesia; Humans; Hypotension; Intervention; Machine learning; Medicine; Meta-analysis; Neural networks; Patients; Perioperative Care - methods; Practice; Randomized Controlled Trials as Topic - methods; Rondomized controlled trial; Surgery; Systematic Review; Anesthesia; Systematic review; Artificial intelligence; Rondomized controlled trial; Meta-analysis
• ISSN: 1471-2253; 1471-2253
• DOI: 10.1186/s12871-024-02699-z

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). 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. 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, I 93.8%). 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. This study was registered with the International Prospective Register of Systematic Reviews (PROSPERO ID: CRD42022353727).