Impact of AI Assistance on Radiologist Accuracy for Lung Nodule Detection on Chest CT.

BACKGROUND: Accurate identification of pulmonary nodules as early-stage lung cancers is crucial to decrease the number of deaths and illnesses caused by lung cancer. Artificial Intelligence has the potential to enhance diagnostic accuracy and specificity in detecting lung cancer. METHODS: Chest CT scanning from 300 patients with an age range between 40 and 80 years old were analysed comparing the pulmonary nodules detection rate (number of lung nodules) between AI-assisted reading, non-AI-assisted reading and the AI-system report standalone. Detected nodules, missed nodules (false negatives), and false-positive findings were analysed. RESULTS: AI-assisted radiologists missed significantly fewer nodules (p<0.001) and achieved an almost perfect correlation (r~1.00) with expert reference values, reducing the mean absolute error (MAE) from 9.78 to 0.46. Additionally, AI increased detection sensitivity from 60% to 98% and reduced false negatives from 3,083 to 145, optimizing both diagnostic accuracy and efficiency. CONCLUSIONS: AI-assisted reading has shown to be beneficial in the detection of lung nodules compared to relying solely on radiologist observation. This suggests that an AI-powered system for evaluating lung nodules has the potential to become a valuable assistant tool in clinical practice. By combining the skills of radiologists with AI assistance, a new approach may emerge, leading to enhanced detection of lung nodules and encouraging the integration of AI in lung cancer screening initiatives.