The integration of Artificial Intelligence (AI) in anesthesiology offers the potential to shift patient monitoring from reactive to predictive. Deep learning architectures, specifically Long Short-Term Memory (LSTM) networks, excel at processing complex, time-series data to forecast future clinical states.

While standard PK/PD models (such as the state of the art Eleveld model for Propofol and Remifentanil) estimate target-site drug concentrations (Ce), they do not account for real-time, patient-specific dynamic responses. This study aims to deploy an AI framework designed to predict future physiological states.

Inclusion Criteria:

• Patients scheduled for elective surgery requiring general anesthesia.
• Procedures requiring continuous depth of anesthesia monitoring (BIS).

Exclusion Criteria:

- Procedures where the primary anesthetic plan does not involve continuous electronic data capture.

Patients undergoing general anesthesia under continuous depth of anesthesia monitoring.