ReviewCureus2026
Artificial Intelligence in Low-Dose Computed Tomography Lung Cancer Screening: Clinical Integration, Validation, and Translational Challenges.
Review in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung cancer remains the leading cause of cancer-related mortality worldwide, largely due to late-stage diagnosis. Low-dose computed tomography (LDCT) screening has demonstrated significant mortality reduction in high-risk populations; however, its widespread implementation is limited by high false-positive rates, inter-reader variability, and substantial workflow burden. Artificial intelligence (AI) has emerged as a promising adjunct to address these challenges by enhancing diagnostic consistency, efficiency, and risk stratification in LDCT-based screening. This narrative review aims to synthesize current evidence on AI methodologies, clinical applications, validation studies, and translational challenges in LDCT-based lung cancer screening. A structured literature search was conducted across PubMed, Scopus, Embase, and the Cochrane Library for studies published between January 2010 and September 2025, using relevant keywords related to AI, radiomics, and lung cancer screening. Studies were selected based on their focus on AI applications in LDCT, including detection, characterization, risk prediction, and workflow optimization. Recent advances in deep learning and radiomics have enabled automated detection, segmentation, and characterization of pulmonary nodules with performance comparable to expert radiologists. Hybrid AI models that integrate imaging-derived features with clinical and demographic data further improve individualized risk prediction and support tailored screening strategies. AI-supported workflows have demonstrated improved efficiency by reducing interpretation time while maintaining diagnostic accuracy. Despite these advances, translation into routine clinical practice remains inconsistent due to limitations in external validation, generalizability, interpretability, and workflow integration. Radiologists' trust and human-AI interaction further influence real-world adoption. This review highlights the need to shift focus from algorithmic performance to clinical integration and human-AI collaboration to ensure meaningful improvements in lung cancer screening outcomes.
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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.