ReviewInternational journal of physiology, pathophysiology and pharmacology2025
Artificial intelligence in automated detection of lung nodules: a narrative review.
Review in International journal of physiology, pathophysiology and pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Diagnostic performance of an artificial intelligence algorithm for detecting pneumoperitoneum on abdominal CT scans.Insights into imaging · 2026Article
- A comparative study of artifact reduction techniques in metal-implanted CT scans.International journal of physiology, pathophysiology and pharmacology · 2026Review
- Comparative risk of osteoporotic fractures with direct oral anticoagulants versus vitamin K antagonists in atrial fibrillation patients: a systematic review.International journal of burns and trauma · 2025Review
- Tuberculous spondylodiscitis with ureteral involvement: a rare case report.American journal of clinical and experimental urology · 2025Article
- Using ultrasonographic features in pediatric Crohn's disease activity index severity.International journal of physiology, pathophysiology and pharmacology · 2025Article
- Neurobrucellosis presenting as acute stroke with brain abscesses: a case report.American journal of neurodegenerative disease · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung cancer remains a leading cause of cancer-related mortality worldwide, and early detection is essential for improving patient outcomes. This study evaluates the role of artificial intelligence (AI) in lung nodule detection, focusing on its potential to enhance the accuracy of early lung cancer diagnosis. We assess the performance of AI tools, particularly convolutional neural networks (CNNs), in identifying and segmenting lung nodules from computed tomography (CT) and X-ray images. Our findings indicate that AI systems achieve a sensitivity of 70-90%, comparable to that of experienced radiologists, while reducing false-positive rates. In pulmonary nodule detection on CT scans, AI demonstrated over 95% sensitivity with fewer than one false-positive per scan. The implementation of AI as a "second reader" significantly improved detection accuracy. Despite these advancements, challenges remain, including high false-positive rates, issues with generalizability across diverse populations, regulatory concerns, and skepticism among healthcare professionals. This study highlights the promise of AI in supporting radiologists and improving lung cancer screening while emphasizing the need for further research to enhance specificity and address existing limitations.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.