Applications of artificial intelligence in thoracic imaging: a review

Authors: Arjun Kalyanpur, Neetika Mathur

Abstract

Artificial intelligence (AI) is transforming the field of radiology. Among various radiologic subspecialties, thoracic imaging has seen a significant rise in demand due to the global increase in heart, vascular, lung, and thoracic diseases such as lung cancer, pneumonia, pulmonary embolism, and cardiovascular diseases. AI promises to revolutionize radiologic diagnostics by enhancing detection, improving accuracy, and reducing the time required to interpret images. It leverages deep learning algorithms, particularly convolutional neural networks, which are increasingly integrated into thoracic imaging workflows to assist radiologists in diagnosing and evaluating heart, vascular, lung, and thoracic diseases. AI systems can help radiologists identify subtle findings that might otherwise be overlooked, thereby increasing efficiency and reducing diagnostic errors. Studies have shown that several AI algorithms have been trained to detect acute chest conditions such as pulmonary embolism, aortic dissection, pneumonia, rib fractures, and lung nodules with high sensitivity and specificity, offering substantial benefits in emergency and high-workload environments. This review article focuses on acute conditions presenting as acute chest syndrome or trauma in emergency settings. It provides an overview of AI applications in thoracic imaging, focusing on advancements in screening, early disease detection, triage and prioritization, automated image analysis, and workflow optimization. These points are supported by review articles published on the subject, including our own publications. We further explore challenges such as regulatory barriers, interpretability, and the need for large, diverse datasets. Finally, we discuss future directions for AI in thoracic imaging, highlighting its potential to enhance patient outcomes and healthcare system efficiencies.