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 aims to assess whether AI models can serve as decision-support tools in perioperative and critical care settings by improving the interpretation of complex hemodynamic data, potentially enhancing patient safety, diagnostic accuracy, and clinical efficiency.
Age ≥ 18 years
Undergoing elective surgery with arterial waveform monitoring as part of standard perioperative care
Hemodynamic data successfully recorded using the MostCare hemodynamic monitoring system
Able to provide informed consent to participate in the study
Incomplete or corrupted hemodynamic data (e.g., signal artifacts preventing reliable analysis)
Emergency surgery cases
Patients with severe arrhythmias or hemodynamic instability that might interfere with arterial waveform interpretation
Refusal to participate or withdrawal of consent