Clinical Evaluation of an AI-Assisted Decision Support System for General Anesthesia Management Based on Data From 6 Centers: Comparative Study.
AI decision-support system showed moderate overall agreement with anesthesiologists' choices.
Clinical Evaluation of an AI-Assisted Decision Support System for General Anesthesia Management Based on Data From 6 Centers: Comparative Study.
AI is rapidly transforming medical practice, with emerging applications in perioperative care and anesthesiology.
This study aimed to assess the performance and clinical applicability of an AI-assisted decision support system (ZW-AA-001) for general anesthesia management by comparing its decisions with those of experienced anesthesiologists across 6 medical centers.
A multicenter retrospective study was conducted using perioperative data from 1008 patients who underwent elective noncardiac surgeries under total intravenous anesthesia.
The study included 1008 patients, with a median age of 50 (IQR 37-59) years and female predominance (619/1008, 61.4%).
The AI system showed significantly faster decision-making time compared to anesthesiologists for the adjustment of propofol (pseudomedian difference -77.5, 95% CI -79.5 to -75.5 seconds; P<.001).
Although esmolol-related decisions showed a numerically higher concordance of 71.4%, this was not statistically significant (PABAK=0.429; P=.21; AC1=0.622; P=.09).
Decisions for atropine, ephedrine, and urapidil demonstrated substantially lower agreement (17.4%-29.8%).
The AI-assisted decision support system demonstrated varying levels of concordance with anesthesiologists in managing surgery, with the highest agreement observed in propofol administration.
However, the system's lower agreement in hemodynamic medication management highlights the need for further optimization and validation.
These findings underscore the potential of AI systems to enhance anesthetic decision-making, improve efficiency, and address workforce challenges in anesthesiology.