Artificial Intelligence in Pulmonary Imaging: A Review of Techniques

Ewelina Jamrozik

doi:10.12775/JEHS.2025.85.66817

Authors

Ewelina Jamrozik Medical College of Rzeszow University https://orcid.org/0009-0001-0118-5823

DOI:

https://doi.org/10.12775/JEHS.2025.85.66817

Keywords

artificial intelligence, pulmonary imaging, deep learning, radiology, medical image analysis, lung diseases, computer-aided diagnosis, thoracic imaging, clinical decision support

Abstract

Introduction and Purpose

The use of artificial intelligence (AI) in medical imaging has grown significantly in recent years, especially in the area of pulmonary imaging. AI systems have shown promising performance in a variety of thoracic imaging tasks, such as nodule detection, classification of interstitial lung diseases, and prognostic modeling. The purpose of this review is to provide a comprehensive overview of the current state of AI applications in pulmonary imaging, and to highlight clinical use cases, significant technological advancements.

Materials and Methods

This review is based on a comprehensive analysis of scientific literature from scientific databases (PubMed, Scopus, Web of Science, and IEEE Xplore), selected based on their citation impact, scientific quality, and relevance to the topic of AI in pulmonary imaging.

Results

An analysis of selected literature has revealed the rapid development of AI applications in pulmonary imaging in recent years. The identified studies cover a wide range of topics, from diagnostic support and automated lesion detection to segmentation of anatomical structures and prognostic modeling. The overwhelming majority of publications have demonstrated the high performance of algorithms for specific imaging tasks. Many have compared AI performance to that of radiologists, often indicating comparable or superior precision.

Conclusion

In pulmonary imaging, AI holds great promise for enhancing diagnostic precision, effectiveness, and decision support. The majority of models are still in the experimental stage, despite numerous studies reporting high performance, particularly in detection and classification tasks. Additional validation, standardization, and consideration of ethical issues are required to facilitate clinical adoption.

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