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Evaluation of the Success of Artificial Intelligence Models in Interpreting Arterial Waveform Analysis Data

NCT06828575 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 145

Last updated 2025-03-04

No results posted yet for this study

Summary

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.

Conditions

  • Hemodynamic Instability

Interventions

OTHER

predictions

predictions of learning language models

Sponsors & Collaborators

  • Kanuni Sultan Suleyman Training and Research Hospital

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-02-15
Primary Completion
2025-08-15
Completion
2025-08-16

Countries

  • Turkey (Türkiye)

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06828575 on ClinicalTrials.gov