ArticleCHEST pulmonary2026
Theoretical Clinical Utility of Advanced Practice Provider Use of an Artificial Intelligence Radiomics-based Tool for Pulmonary Nodule Evaluation and Management.
Article in CHEST pulmonary, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Background: As integral members of pulmonary nodule (PN) programs, advanced practice providers (APPs) routinely evaluate PN cancer risk and make management recommendations. It is unknown whether an artificial intelligence (AI) tool impacts APP PN assessment and decision-making. Research Question: What is the theoretical effect of APP use of a commercially available AI radiomics-based computer-aided diagnosis tool on PN diagnostic accuracy and management decision-making? Study Design and Methods: In this retrospective multi-reader multi-case study performed from May 2024 to June 2024, 6 APP "readers" (4 in pulmonology, 2 in thoracic surgery) independently evaluated 300 chest CT scan "cases", each with an indeterminate PN 5-30 mm in maximal diameter (50% cancer prevalence). Using solely CT imaging data, APPs provided an estimate of cancer risk and management recommendation for each case without and then with AI tool assistance. The effect of the AI tool on readers' diagnostic performance and management decisions was assessed using descriptive statistics, area under the receiver operating characteristic curve (AUC), and reclassification plots and tables. Results: With AI tool assistance, APP readers' average PN diagnostic accuracy increased by 9 percentage points (AUC: 0.79 vs 0.88; Interpretation: APP use of a commercially available AI radiomics-based tool for PN evaluation was associated with increased diagnostic accuracy and invasive diagnostic procedure recommendation for malignant PNs. Future prospective, randomized clinical trials are required to assess its use in routine clinical practice.
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Registered trials
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