Application of Artificial Intelligence in Radiological Image Analysis for Pulmonary Disease Diagnosis: A Review of Current Methods and Challenges
Introduction and purpose Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), is revolutionizing radiology by improving diagnostic accuracy and efficiency. This paper examines AI applications, especially convolutional neural networks (CNNs), in diagnosing pulmona...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Kazimierz Wielki University
2025-01-01
|
| Series: | Journal of Education, Health and Sport |
| Subjects: | |
| Online Access: | https://apcz.umk.pl/JEHS/article/view/56893 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Introduction and purpose
Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), is revolutionizing radiology by improving diagnostic accuracy and efficiency. This paper examines AI applications, especially convolutional neural networks (CNNs), in diagnosing pulmonary diseases, such as pneumonia, tuberculosis, and lung cancer. The goal is to explore the impact of these technologies and assess challenges in their integration into clinical practice.
Material and methods
This review is based on articles from the PubMed database, published between 2015 and 2024, using keywords such as artificial intelligence in radiology, AI in medicine, AI in chest X-ray, and AI in chest-CT.
Results
AI, driven by ML and DL, has significantly enhanced medical imaging analysis, automating tasks that require expert interpretation. CNNs excel in processing raw image data and identifying hierarchical features, surpassing traditional methods in diagnosing lung diseases from radiographs and CT scans. AI systems demonstrate exceptional accuracy in detecting pneumonia, tuberculosis, and lung cancer, providing rapid, consistent results, particularly valuable in resource-limited settings. However, challenges persist, including the need for diverse training datasets, model interpretability, and integration into existing workflows.
Conclusions
AI, especially CNN-based DL models, is reshaping radiology by advancing diagnostic capabilities. While it often outperforms traditional methods, AI is best used to complement human expertise. Overcoming challenges in data quality, system integration, and training is essential for broader clinical adoption. Continued research will enhance AI’s reliability and utility, ultimately improving patient outcomes.
|
|---|---|
| ISSN: | 2391-8306 |