Diagnostic accuracy and cost-efficiency of ChatGPT in EIT-based pendelluft detection for mechanically ventilated patients: a multicenter study

Abstract

Introduction: Electrical impedance tomography (EIT) enables real-time bedside monitoring of regional lung ventilation, but its clinical adoption is limited by complex data interpretation requiring substantial expertise and time. Pendelluft—the asynchronous movement of air between lung regions—is an important indicator of ventilation heterogeneity and poor outcomes in mechanically ventilated patients. There remains an unmet need for accurate, efficient, and clinically interpretable automated pendelluft detection tools. Methods: In this retrospective multicenter study, we developed and validated an automated system for pendelluft detection using a ChatGPT-generated deep learning architecture. Consecutive mechanically ventilated adults from three tertiary hospitals in China (January 2020–December 2024) were screened; 278 patients met inclusion criteria. High-resolution EIT signals were processed using a hybrid model—the initial code generated by ChatGPT (GPT-4), further optimized by clinicians—which included a modified ResNet-34 encoder, bidirectional LSTM with self-attention, and Grad-CAM interpretability with integrated safety workflow. Primary outcomes were diagnostic accuracy (sensitivity, specificity, AUC-ROC) and analytic efficiency (processing time, cost per case) compared to expert review and conventional machine learning models. Results: The ChatGPT-generated system demonstrated superior diagnostic accuracy for automated pendelluft detection, yielding an AUC-ROC of 0.91 (95% CI: 0.87–0.95), sensitivity of 89.6%, and specificity of 92.1%. These metrics significantly exceeded those of standard CNN (AUC 0.85), random forest (AUC 0.82), and SVM (AUC 0.79) models. Median analysis time per case was reduced from 12.5 min (manual expert review) to 2.8 min (AI system), with an average cost saving of 200 RMB per patient. Higher pendelluft grades correlated strongly with worse clinical outcomes, including increased 28-day mortality (25.4% vs. 9.0%, adjusted HR 2.8, 95% CI: 1.6–4.9, P = 0.001), fewer ventilator-free days, and greater risk of complications. The workflow demonstrated high operational reliability, robust safety, and strong agreement with clinician assessments (Cohen’s κ = 0.86). Conclusions: The ChatGPT-generated AI system offers an accurate, rapid, and cost-effective solution for automated EIT analysis and pendelluft detection in mechanically ventilated patients. This novel workflow facilitates real-time, explainable clinical decision support, and may help optimize ventilator management and improve patient outcomes.