ArticlePloS one2026
Real-world before-and-after evaluation of AI support for lung cancer diagnosis at three US lung nodule clinics.
Article in PloS one, 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
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHealth systems and payers require evidence that artificial intelligence (AI)-enabled decision support improves care delivery. Integrating AI into lung nodule management pathways may streamline workflows, improve identification and triage of patients with pulmonary nodules, and enable earlier lung cancer diagnosis. Yet real-world evidence of clinical utility remains limited. This study evaluated the impact of implementing commercially available AI software to support the lung nodule and lung cancer care pathway across three US health systems. MATERIALS AND
methodsA real-world before-and-after study compared clinic activity during matched 12.5-month pre-implementation and post-implementation periods at three US lung nodule clinics. Primary outcomes were the number of new patients and the number of patients undergoing at least one invasive procedure. Secondary outcomes were numbers of lung cancers and early-stage (stages I-II) lung cancers, and time from baseline CT to diagnosis. Analyses used Wilcoxon signed-rank and Mann-Whitney U tests, with medians and interquartile ranges (IQRs) reported for continuous measures.
resultsAcross sites, new patients increased from 138 before to 232 after implementation, equivalent to 11.0 to 18.6 patients per month (p = 0.0002), with the median rising from 9 (IQR 7-14) to 16 (IQR 13-21) patients. Monthly patients undergoing at least one invasive procedure increased from 4.9 to 8.0 (p = 0.0085). Monthly cancers increased from 3.0 to 3.8 (p = 0.3242), while monthly early-stage cancers rose from 1.4 to 2.4 (p = 0.125). Median time to diagnosis was 58 days (IQR 33-158) before and 57 days (IQR 27-85) after implementation (p = 0.26), with IQR narrowing from 125 days to 58 days.
conclusionImplementation of the AI-enabled software was associated with significantly increased throughput of lung nodule patients and procedural volume. These results, observed prospectively post-implementation, suggest AI-assisted identification and triage can expand access to timely specialist review.
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.