ArticlePloS one2026
Cost-effectiveness analysis of artificial intelligence-assisted risk stratification of indeterminate pulmonary nodules.
Article in PloS one, 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
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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.
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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.
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Who cites it
2 citing papers in PubMed.
- Assessing the Value for Money of AI-Assisted Technologies for Older Adults: Scoping Review of Economic Evaluations.Journal of medical Internet research · 2026Article
- Recent advances in artificial intelligence across interventional pulmonology: a narrative review.Journal of thoracic disease · 2026Review
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
backgroundArtificial intelligence-based radiomic approaches have been shown to accurately evaluate indeterminate pulmonary nodules. With the expansion of lung cancer screening and utilization of computed tomography imaging, indeterminate pulmonary nodules requiring diagnostic evaluation are increasingly common. Accurate non-invasive characterization may reduce time to cancer diagnosis and decrease invasive procedures for benign disease, but the cost-effectiveness of AI-based methods has not been quantified. We sought to evaluate the cost-effectiveness of AI-assisted clinician evaluation compared to clinician evaluation alone for the cancer risk stratification of patients with indeterminate pulmonary nodules.
methodsWe constructed a decision model assuming guideline-based care from a payer perspective with a lifetime horizon. The base case is a 1.1 cm incidentally discovered IPN in a 60-year-old operative candidate in a clinical population with a 65% malignancy prevalence. Cost per life-year gained (LYG) was the primary outcome. We conducted deterministic sensitivity analyses on all parameters and performed a probabilistic sensitivity analysis. Given clinical variability of malignancy prevalence, we assessed the malignancy prevalence threshold at which utilization of AI would be cost-effective.
resultsAI-supported clinician risk stratification resulted in an increase of 0.03 life years compared to clinician alone. With a 65% malignancy prevalence, AI was cost-effective with an incremental cost-effectiveness ratio (ICER) of $4,485/LYG. When the malignancy prevalence was < 5%, the ICER for AI support exceeded a standard willingness-to-pay threshold of $100,000/LYG.
conclusionsIn clinical settings with a pre-test probability of malignancy exceeding 5%, AI-supported IPN risk stratification is cost-effective compared to clinician assessment alone.
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