Artificial intelligence for detection and characterization of pulmonary nodules in lung cancer CT screening: ready for practice?

Lung cancer computed tomography (CT) screening trials using low-dose CT have repeatedly demonstrated a reduction in the number of lung cancer deaths in the screening group compared to a control group. With various countries currently considering the implementation of lung cancer screening, recurring discussion points are, among others, the potentially high false positive rates, cost-effectiveness, and the availability of radiologists for scan interpretation. Artificial intelligence (AI) has the potential to increase the efficiency of lung cancer screening. We discuss the performance levels of AI algorithms for various tasks related to the interpretation of lung screening CT scans, how they compare to human experts, and how AI and humans may complement each other. We discuss how AI may be used in the lung cancer CT screening workflow according to the current evidence and describe the additional research that will be required before AI can take a more prominent role in the analysis of lung screening CT scans.

Automated summary

Lung cancer CT screening has shown a reduction in deaths but faces challenges such as false positives, cost-effectiveness, and radiologist availability. AI can enhance efficiency in screening, but more research is needed to fully integrate it into the analysis of lung CT scans.