Leveraging Artificial Intelligence to Transform Thoracic Radiology for Lung Nodules and Lung Cancer: Applications, Challenges, and Future Directions.
Authors
Affiliations (4)
Affiliations (4)
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine.
- Department of Radiology and Medical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan.
- Department of Electronics Engineering, Incheon National University, Incheon, South Korea.
- Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul.
Abstract
This review traces the historical path of artificial intelligence (AI) methods that have been applied to medical image interpretation. Early AI approaches, which were based on clinical expertise and domain-specific medical knowledge, established the basis for data-driven methods, initiating the radiomics era and leading to the widespread use of deep learning in medical imaging. More recently, transformer architectures-originally developed for natural language processing-have been adapted for medical image analysis. In the first section, we explore the literature on the use of AI, specifically addressing lung nodules and lung cancer. AI has been effective in detecting lung nodules, evaluating their characteristics, and predicting cancer risk, while also addressing technical issues like kernel conversion. In lung cancer, AI has been applied to various clinical needs, including prognosis evaluation, mutation identification, treatment response analysis, operability prediction, treatment-related pneumonitis, and clinical information extraction. In the following section, we explore foundation models, multimodal AI, and a multiomic approach in the field of lung nodules and lung cancer. Finally, as AI models continue to evolve, so too must the approaches for evaluating their real-world utility; thus, we outline relevant methods for evaluating the performance and application of AI in thoracic radiology.