ARTIFICIAL INTELLIGENCE IN PATIENT MONITORING AND PREDICTION OF PERIOPERATIVE COMPLICATIONS

Abstract

Background: Perioperative complications, including sepsis, acute kidney injury (AKI), and hemodynamic instability, continue to be primary drivers of morbidity and mortality among surgical patients. Conventional monitoring techniques and risk stratification scores frequently lack the sensitivity required to detect early physiological deterioration, often resulting in delayed interventions. The integration of Artificial Intelligence (AI) and Machine Learning (ML) into clinical practice presents a significant opportunity to enhance patient safety through the use of predictive analytics.

Objectives: This narrative review evaluates the current landscape of AI-driven technologies in perioperative care. It specifically focuses on the capacity of these tools to predict critical complications, including hypotension, sepsis, and AKI, and assesses their potential to refine clinical decision-making relative to traditional methods.

Methods: A comprehensive review of the literature was conducted based on specific inclusion criteria. The analysis synthesizes findings from recent studies that compare ML algorithms, such as Gradient Boosting, Random Forest, and Deep Learning, as well as AI-based tools like the Hypotension Prediction Index (HPI), against standard care protocols and established risk scoring systems.

Results: AI models consistently demonstrate superior performance in predicting perioperative adverse events when compared to traditional methods. Notably, the Hypotension Prediction Index (HPI) has been shown to significantly reduce both the duration and severity of intraoperative hypotension. Furthermore, ML algorithms exhibit high accuracy in the early prediction of sepsis and acute kidney injury, frequently outperforming standard clinical scores such as SOFA or ASA physical status. Despite these successes, challenges persist regarding data heterogeneity, algorithm interpretability, and the necessity for extensive external validation.

Conclusion: Artificial intelligence represents a transformative instrument in perioperative medicine that facilitates a shift from reactive treatments to proactive patient management. Although current evidence supports the efficacy of AI in predicting complications, successful clinical implementation depends on addressing ethical concerns, enhancing model generalizability, and ensuring seamless integration into existing clinical workflows.