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The goal of this observational study is to evaluate the ability of artificial intelligence (AI) models to interpret arterial waveform analysis data obtained from a hemodynamic monitoring system in adult patients undergoing elective surgery. The main questions it aims to answer are: Can AI models (ChatGPT-4 and Gemini 2.0) accurately detect hemodynamic abnormalities in arterial waveform data? How well do AI-generated diagnoses align with expert anesthesiologist assessments? Are AI-generated treatment recommendations clinically appropriate? Participants will: Undergo standard hemodynamic monitoring with an arterial waveform analysis device (MostCare). Have their anonymized hemodynamic data analyzed by AI models for abnormality detection, diagnosis suggestions, and treatment recommendations. Have AI-generated results reviewed and validated by experienced anesthesiologists. This study
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