Cost-effectiveness analysis of artificial intelligence-assisted risk stratification of indeterminate pulmonary nodules
In clinical settings with a pre-test probability of malignancy exceeding 5%, AI-supported IPN risk stratification is cost-effective compared to clinician assessment alone.
Authors
- Ashley A. Leech (ORCID: https://orcid.org/0000-0001-6795-929X)
- Stephen A. Deppen (ORCID: https://orcid.org/0000-0002-3661-662X)
- Evan C. Osmundson
- Caroline M. Godfrey (ORCID: https://orcid.org/0000-0002-4090-2338)
- Sophia M. Pena (ORCID: https://orcid.org/0000-0003-4981-4024)
- L. Pickup (ORCID: https://orcid.org/0000-0003-3183-297X)
- Hannah N. Marmor (ORCID: https://orcid.org/0000-0001-8385-3102)
- Eric L. Grogan
- Fabien Maldonado
- Stacie B. Dusetzina
- Kevin C. McGann
- Jinyi Zhu
Institutions
- Vanderbilt University (US)
- VA Tennessee Valley Healthcare System (US)
- Vanderbilt Health (US)
- Shell (United Kingdom) (GB)
- Vanderbilt University Medical Center (US)
Publication Details
- Journal
- PLoS ONE
- Published
- 2026-03-05
- DOI
- https://doi.org/10.1371/journal.pone.0343492
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Health Services Research and Development
- National Institutes of Health