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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Domain-Specific and Computer-Vision-Driven Versus General-Purpose AI Models in PA-CXR Analysis: a Comparative Study

Yunus Dogan¹, Adem Az², Ozgur Sogut¹

¹Department of Emergency Medicine, Haseki Training and Research Hospital, University of Health Sciences, Uğur Mumcu Mah. Belediye Sok. No:7 Sultangazi, Istanbul, Turkey.

Journal of Imaging Informatics in Medicine

|January 6, 2026

Summary

Qure.ai demonstrated superior diagnostic accuracy in interpreting chest X-rays compared to emergency medicine specialists and other AI models. This AI system shows significant potential for clinical use in medical imaging analysis.

Keywords:

Artificial intelligence Chest X-ray Diagnostic accuracy Domain-specific AI Emergency medicine

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Area of Science:

Artificial Intelligence in Medical Imaging
Radiology Diagnostics
Machine Learning for Healthcare

Background:

Accurate interpretation of PA-CXRs is crucial for emergency medicine.
Evaluating AI diagnostic performance against human experts is essential for clinical integration.
Existing AI models show variable accuracy in complex medical image analysis.

Purpose of the Study:

To compare the diagnostic accuracy of three AI models (GPT-5, Xray-GPT, Qure.ai) against emergency medicine specialists (EMSs).
To assess AI performance in interpreting PA-CXRs from real-world emergency department cases.
To identify AI systems with potential for expert-level diagnostic capabilities.

Main Methods:

Prospective, cross-sectional diagnostic accuracy study using a standardized test set of 40 PA-CXRs.
Evaluation of 30 EMSs and three AI models (GPT-5, Xray-GPT, Qure.ai) over 30 days.
Comparison of diagnostic accuracy across six categories, including life-threatening conditions.

Main Results:

Qure.ai achieved the highest overall accuracy, significantly outperforming EMSs and GPT-based models (p < 0.001).
GPT-5 and Xray-GPT showed lower accuracy than EMSs across most case categories.
Qure.ai excelled in identifying critical findings like pneumonia and pneumothorax, matching EMS performance for normal CXRs and specific pathologies.

Conclusions:

Qure.ai is the only AI system to demonstrate diagnostic performance comparable to or exceeding human experts in PA-CXR interpretation.
Domain-specific training and optimized architecture are key to achieving high clinical value in AI diagnostic tools.
AI systems like Qure.ai hold significant promise for enhancing diagnostic capabilities in emergency radiology.