Artificial Intelligence and its future potential in lung cancer screening.

Area of Science:

• Medical imaging analysis
• Radiology
• Artificial intelligence in healthcare

Background:

• Artificial intelligence (AI) can interpret complex medical data, aiding in diagnostics.
• AI in imaging diagnostics can reduce radiologist workload and improve lung cancer screening sensitivity.

Purpose of the Study:

• To evaluate the role of AI in lung cancer screening.
• To assess the future potential and efficiency of AI in nodule classification.

Main Methods:

• Systematic review of relevant studies published between 2010-2020 from PubMed.
• Exclusion of animal studies.
• Analysis of AI's contribution to lung cancer screening and nodule classification.

Main Results:

• Deep learning and machine learning techniques enable automatic nodule characterization and classification with high precision.
• Several high-performance AI models have been proposed, but a validated model for routine use requires further development.
• Combining AI with radiologist expertise leads to improved accuracy in lung cancer screening.

Conclusions:

• AI integration with radiology can significantly enhance sensitivity, specificity, and accuracy in lung cancer screening and nodule classification.
• Further research and model validation are necessary to determine the feasibility of routine AI integration in clinical practice.