ArticleBMJ open respiratory research2025
Employment of artificial intelligence for early lung cancer diagnosis: a retrospective cohort study.
Article in BMJ open respiratory research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Pulmonary nodules: classification, biomarkers, signaling pathways, diseases, early diagnosis and management.Signal transduction and targeted therapy · 2026Review
- Artificial intelligence-assisted early screening of lung cancer and accurate diagnosis of pulmonary nodules: research progress and clinical prospects from radiomics to multi-omics integration: a narrative review.Journal of thoracic disease · 2026Review
- Artificial intelligence for ultrasound-based differentiation of soft tissue liposarcoma from benign masses.Frontiers in medicine · 2026Article
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimThis 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
methodsWe 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.
resultsThe 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).
conclusionThese 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.
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