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    Artificial intelligence (AI) offers proactive hemodynamic management during surgery by predicting hypotension before it occurs. AI tools analyze patient data to enable earlier interventions, potentially improving surgical outcomes.

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    Area of Science:

    • Anesthesiology
    • Medical Informatics
    • Artificial Intelligence

    Background:

    • Intraoperative hypotension is linked to adverse postoperative outcomes.
    • Current management of hypotension is often reactive, occurring after the event.
    • Artificial intelligence (AI) is emerging as a tool to transform perioperative medicine.

    Purpose of the Study:

    • To provide an overview of AI in intraoperative hemodynamic monitoring and management.
    • To discuss the benefits, limitations, and future directions of AI in this field.
    • To explore AI-based predictive tools for proactive hypotension management.

    Main Methods:

    • Review of AI concepts and applications in perioperative medicine.
    • Analysis of AI-based predictive algorithms like the Hypotension Prediction Index (HPI).
    • Discussion of preliminary clinical study findings and challenges to implementation.

    Main Results:

    • AI-based tools can analyze physiological signals to predict imminent hypotension.
    • Early studies suggest AI support may reduce the depth and duration of hypotensive episodes.
    • AI shows potential in optimizing blood pressure monitoring and echocardiographic interpretation.

    Conclusions:

    • AI can complement clinical expertise in intraoperative decision-making.
    • Predictive AI models may facilitate a more anticipatory approach to hemodynamic management.
    • Further multicenter trials are needed to confirm clinical benefits and patient outcomes.