Lung cancer remains one of the leading causes of cancer-related mortality worldwide, and early detection is critical for improving patient outcomes. Traditionally, radiologists have played a pivotal role in diagnosing lung cancer through imaging modalities such as chest computed tomography (CT). However, the recent integration of artificial intelligence (AI) into medical imaging has introduced a promising alternative or adjunct to human interpretation. AI algorithms, particularly those based on deep learning, have demonstrated substantial capability in identifying pulmonary nodules and other lung abnormalities with high sensitivity and specificity.

Several studies have compared the diagnostic accuracy of AI-assisted detection to that of radiologists. A notable study by Wu et al. involving over 23,000 patients undergoing low-dose CT screening found that AI systems consistently demonstrated higher positive detection rates across all screening rounds compared to manual readings by radiologists. These findings suggest that AI may reduce the rate of missed diagnoses, particularly in the early stages of lung cancer when lesions are subtle and easily overlooked.

Despite these advancements, AI is not without limitations. Factors such as dataset bias, variability in image quality, and the lack of contextual clinical understanding can affect the performance of AI systems. Moreover, AI cannot replace the nuanced clinical judgment and holistic patient evaluation provided by experienced radiologists.