Employment of artificial intelligence for early lung cancer diagnosis: a retrospective cohort study.

AIM: This study aims to evaluate the effectiveness of combined artificial intelligence (AI)-based tools for early patient identification, risk stratification and tracking in increasing the follow-up rate of incidentally detected lung nodules, potentially leading to earlier diagnoses of lung cancer, particularly non-small cell lung cancer (NSCLC). PATIENTS AND METHODS: We conducted a retrospective cohort study involving all patients who underwent CT scans at an academic medical centre over an 8-month period. Real-world practice was compared with modelling of a hypothetical intervention with AI tools. This study was complemented by a multi-reader multi-case analysis to enhance the robustness of our findings. RESULTS: The implementation of AI tools significantly increased the rates of guideline-concordant follow-up for detected nodules, rising from 34% without the tool to 94% with the AI intervention (p<0.0001, McNemar's test). Furthermore, the median time to diagnosis of NSCLC was reduced from 129 days to 25 days (p<0.001, Wilcoxon signed-rank test). CONCLUSION: These findings provide compelling evidence that AI tools can enhance the follow-up rates for patients with incidentally detected lung nodules and expedite the diagnosis of lung cancer. The integration of AI in clinical practice may significantly improve patient outcomes in lung cancer detection and management.