Leveraging Artificial Intelligence to Transform Thoracic Radiology for Lung Nodules and Lung Cancer: Applications,...

Area of Science:

• Medical Imaging Analysis
• Artificial Intelligence in Radiology
• Thoracic Radiology

Background:

• Historical evolution of AI in medical image interpretation, from expert systems to data-driven methods.
• The rise of radiomics and deep learning in medical imaging.
• Adaptation of transformer architectures for medical image analysis.

Purpose of the Study:

• To review the historical progression of AI applications in medical image interpretation.
• To explore AI's role in lung nodule and lung cancer detection, characterization, and risk prediction.
• To discuss emerging AI approaches like foundation models and multimodal AI in thoracic radiology.

Main Methods:

• Literature review of AI methods applied to medical image interpretation.
• Focus on AI applications in lung nodule and lung cancer analysis.
• Exploration of foundation models, multimodal AI, and multiomic approaches.
• Discussion of methods for evaluating AI performance in real-world clinical settings.

Main Results:

• AI demonstrates effectiveness in detecting lung nodules, assessing characteristics, and predicting cancer risk.
• AI is utilized for various lung cancer clinical needs, including prognosis, mutation identification, and treatment response.
• Transformer architectures and advanced AI models show promise in medical image analysis.

Conclusions:

• AI has significantly advanced medical image interpretation, particularly in thoracic radiology.
• The continuous evolution of AI necessitates robust evaluation methods for clinical utility.
• Future directions include foundation models, multimodal AI, and multiomic integration for lung nodule and cancer analysis.