ARTIFICIAL INTELLIGENCE–BASED PRECISION ANESTHESIA: ADAPTIVE DOSING MODELS FOR PATIENT-SPECIFIC MANAGEMENT

Abstract

The administration of anesthesia requires precise dosing to balance patient safety and procedural efficacy. Traditional methods often rely on standardized protocols and clinician experience, which may not account for inter-individual variability in pharmacokinetics and pharmacodynamics. Artificial Intelligence (AI) offers a transformative approach by integrating patient-specific data—such as age, weight, comorbidities, genetic profiles, and real-time physiological parameters—to optimize anesthesia dosing. AI-assisted systems utilize machine learning models and predictive algorithms to recommend individualized doses, reduce adverse events, and enhance recovery outcomes. This thesis explores the application of AI in anesthesia dosage selection, discussing computational frameworks, clinical utility, benefits, limitations, and future directions.