<b>ARTIFICIAL INTELLIGENCE – ASSISTED HEMODYNAMIC MONITORING AND</b><b> </b><b>PERIOPERATI

Nine studies with 1,842 patients were synthesized to evaluate artificial intelligence-assisted hemodynamic monitoring in adult cardiac surgery. This systematic review and meta-analysis, conducted by Said KHALLIKANE and updated in April 2026, found AI monitoring significantly reduced intraoperative hypotension duration by a mean difference of -18.6 minutes and was associated with a -0.8 day reduction in ICU length of stay.

Use Cases

• Benchmarking AI model performance for predicting intraoperative hypotension based on the meta-analysis results.
• Analyzing the relationship between predictive monitoring and short-term clinical outcomes like ICU length of stay.
• Designing clinical trials for AI-assisted monitoring by referencing the identified evidence gaps and methodological heterogeneity.
• Investigating the mechanistic shift from reactive to proactive hemodynamic management supported by the review's findings.

Strengths

• Evidence synthesis follows PRISMA 2020 guidelines, indicating a structured methodological approach.
• Quantitative meta-analysis includes data from three comparable studies, providing pooled effect estimates.
• Analysis integrates data from both randomized controlled trials and observational studies, balancing rigor and applicability.

Limitations

• Quantitative synthesis is limited to a small number of studies (three), which may affect generalizability.
• The dataset is a review document (DOCX); column-level documentation for underlying study data is absent.
• Heterogeneity in AI technologies and study methodologies across the included studies is noted as a limitation.

Provenance

Source

figshare

Collection Method

Systematic review and meta-analysis of published studies.

Time Range

Contemporary evidence up to the review's publication date.

Freshness

Last updated 2026-04-30 14:46:48.

Geography

Global (studies included are not geographically specified).