Artificial intelligence in lung cancer screening: Detection, classification, prediction, and prognosis

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Oncology

Background:

• Artificial intelligence (AI) demonstrates advanced capabilities in medical image analysis and complex model processing.
• Deep learning, particularly convolutional neural networks (CNNs), is expanding AI applications in lung cancer screening.

Purpose of the Study:

• To review the current status of AI technologies in medical imaging.
• To explore AI applications in lung cancer screening, including segmentation, nodule detection, and classification.
• To assess AI's potential in improving nodule detection sensitivity and reducing false positives.

Main Methods:

• Analysis of current AI technologies.
• Exploration of AI applications in lung cancer screening tasks.
• Assessment of AI's impact on nodule detection and false-positive reduction.

Main Results:

• AI shows significant potential in enhancing nodule detection sensitivity and reducing false-positive rates in lung cancer screening.
• AI assists in classifying lung nodules and predicting their growth and pathological/genetic characteristics.

Conclusions:

• AI presents a promising supportive tool for lung cancer screening, enhancing detection and classification.
• Further research is required to improve the universality and interpretability of AI results, focusing on large-scale validation and multi-center studies.