KI-Algorithmen – intraoperatives hämodynamisches Monitoring von morgen?

Abstract: This article provides an overview of basic concepts of artificial intelligence (AI), AI-based approaches in intraoperative hemodynamic monitoring and management, their potential benefits and limitations, and possible future directions. AI is rapidly transforming perioperative medicine through the analysis of the large volumes of data generated during surgery. Intraoperative hypotension is associated with postoperative complications and organ injury, yet current management remains mostly reactive, with interventions typically initiated only after hypotension occurs. AI-based predictive tools are designed to enable anesthesiologists to act proactively before hypotension develops.Algorithms such as the Hypotension Prediction Index (HPI) analyze high-resolution arterial pressure waveforms and other physiological signals to identify patterns indicating imminent hypotensive events. First clinical studies in both cardiac and non-cardiac surgery suggest that AI-guided support can reduce the depth and duration of hypotensive episodes, although larger multicenter trials are needed to determine effects on patient outcomes.Beyond hypotension prediction, AI has potential in optimizing blood pressure measurement, supporting echocardiographic image interpretation, and classifying patients according to hemodynamic phenotypes to guide individualized therapy. Challenges to clinical implementation include data privacy and regulatory requirements, algorithmic bias, explainability, the risk of overreliance on automated recommendations and data-related challenges in model training and validation.Currently, AI does not replace clinical expertise but may complement intraoperative decision-making. Predictive models may allow earlier recognition of patients at risk of hypotension and thereby support a more anticipatory approach to hemodynamic management. The extent to which this translates into measurable clinical benefit requires further investigation.