ARTIFICIAL INTELLIGENCE IN CHEST X-RAY DIAGNOSTICS OF PNEUMONIA: OPPORTUNITIES TO REDUCE MEDICAL ERRORS AND IMPROVE CLINICAL PRACTICE EFFICIENCY

Abstract

Introduction and Purpose: Chest X-ray (CXR) interpretation forms the bedrock of pneumonia diagnosis, yet it remains susceptible to human error and significant variability, with documented error rates reaching up to 30%. Artificial intelligence (AI), particularly through advancements in deep learning, presents a powerful opportunity to enhance diagnostic accuracy, minimize errors, and optimize clinical workflows. This structured review offers a critical summary of AI-based approaches for pneumonia detection on CXRs, delving into their diagnostic metrics, performance comparisons, impact on workflow, and role in error reduction.

Material and Method: We conducted a systematic synthesis of peer-reviewed literature from key databases including PubMed, ScienceDirect, Nature, and MDPI. Our search encompassed multicenter studies, comparative trials involving radiologists, and reports on real-world clinical deployments. Inclusion criteria specifically mandated explicit reporting of sensitivity, specificity, area under the curve (AUC), time savings, detailed dataset characteristics, comprehensive error analysis, and workflow efficiency. Special attention was given to studies involving convolutional neural networks (CNNs—such as ResNet, DenseNet, CheXNet, and Mask R-CNN), multicenter validation, applications in "second-reader" modes and triage systems, and aspects of interpretability.

Results: AI-powered CXR solutions consistently demonstrate high diagnostic value, with AUCs typically ranging from 0.87 to 0.98, and achieving sensitivity/specificity rates of 90–98% and 80–99% respectively. Notably, FDA-cleared platforms exhibit an AUC of 0.976, sensitivity of 0.908, and specificity of 0.887. The CheXNet model achieved diagnostic accuracy on par with radiologists when evaluated on the ChestX-ray14 dataset. Stand-alone AI review systems can process CXRs and generate reports in a mere 3–5 seconds (a dramatic reduction from approximately 1 hour for manual interpretation), significantly accelerating turnaround times and enabling rapid patient triage. When implemented in a "second-reader" capacity, AI tools reduce missed consolidations by up to 98% and effectively elevate the diagnostic accuracy of non-radiologists to a level comparable with that of board-certified radiologists. Furthermore, validation studies across pediatric and multi-pathology cases show robust performance metrics, provided age-appropriate adjustments are applied. However, comprehensive explainability and seamless integration remain crucial for the widespread and sustained adoption of these technologies.

Conclusions: AI, when applied to CXR-based pneumonia detection, demonstrably improves clinical accuracy, expedites reporting, and significantly mitigates human diagnostic error. These benefits are particularly pronounced in high-throughput environments and resource-constrained settings. Future large-scale implementation will depend on transparent validation processes, continuous real-world monitoring, and strong partnerships with clinicians to foster trust, ensure diagnostic consistency, and ultimately achieve optimal patient outcomes.