Real-World Experience With Artificial Intelligence-Enabled Natural Language Processing for Identification and Management of Incidental Lung Nodules
Abstract
Background Incidental lung nodules (ILNs) are common, but adherence to guideline-recommended follow-up is variable, which may contribute to delayed diagnosis of malignancy. Artificial intelligence (AI) platforms using natural language processing (NLP) may improve identification, but their impact on clinical management and outcomes remains uncertain. Objectives This study describes ILN identification, follow-up management, and timeliness of cancer treatment after implementation of an AI-enabled NLP platform at a large health system. Design Single-center retrospective observational study. Methods Adults with ILNs identified on imaging between April 2023 and May 2024 were included. The pre-AI cohort was identified using diagnosis codes, while the post-AI cohort was identified using an NLP-based AI platform. Because these methods captured different populations, no formal comparative testing was performed, and outcomes were summarized descriptively. Results A total of 1,098 patients with ILNs were identified: 228 from 6,828 pre-AI imaging studies (3.3%) and 870 from 9,902 post-AI imaging studies (8.8%). Most pre-AI ILNs were identified on outpatient imaging (74%), while post-AI ILNs were identified in inpatient (35%), emergency department (26%), and outpatient (39%) settings. A follow-up recommendation was documented for 195 of 228 pre-AI patients (85.5%) and 563 of 870 post-AI patients (64.7%). Among these patients, completion was documented for 167 of 195 (85.6%) and 414 of 563 (73.5%), respectively. Malignancy was documented in 28 of 228 pre-AI patients (12.3%) and 15 of 870 post-AI patients (1.7%). Median imaging-to-biopsy time was 78 days pre-AI and 120 days post-AI; biopsy-to-treatment and imaging-to-treatment intervals were similar. Conclusion The post-AI cohort included more identified ILNs and a greater proportion from acute-care settings than the historical pre-AI cohort. Because identification methods were not comparable, observed differences in documented malignancy diagnosis and treatment timing cannot be attributed to the platform. Effective implementation requires defined clinical ownership and follow-up infrastructure.
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BibTeX
@article{ploucher2026realworld,
title = {{Real-World Experience With Artificial Intelligence-Enabled Natural Language Processing for Identification and Management of Incidental Lung Nodules}},
author = {Shelby Ploucher MD and Andrew D. Nguyen PhD and Rida Altaf DO and Zaim Khan MD and Zilipah Cruz MSN-RN, ONN-CG and Luis Herrera MD and Ali Jiwani MD MSc and Jennifer Tseng MD and Kim Styrvoky MD},
journal = {Therapeutic Advances in Respiratory Disease},
year = {2026},
month = oct,
doi = {10.1177/17534666261501246},
url = {https://doi.org/10.1177/17534666261501246},
note = {CC BY},
}
RIS
TY - JOUR
TI - Real-World Experience With Artificial Intelligence-Enabled Natural Language Processing for Identification and Management of Incidental Lung Nodules
AU - Shelby Ploucher MD
AU - Andrew D. Nguyen PhD
AU - Rida Altaf DO
AU - Zaim Khan MD
AU - Zilipah Cruz MSN-RN, ONN-CG
AU - Luis Herrera MD
AU - Ali Jiwani MD MSc
AU - Jennifer Tseng MD
AU - Kim Styrvoky MD
PY - 2026
DA - 2026/10/28
JO - Therapeutic Advances in Respiratory Disease
DO - 10.1177/17534666261501246
UR - https://doi.org/10.1177/17534666261501246
AB - Background Incidental lung nodules (ILNs) are common, but adherence to guideline-recommended follow-up is variable, which may contribute to delayed diagnosis of malignancy. Artificial intelligence (AI) platforms using natural language processing (NLP) may improve identification, but their impact on clinical management and outcomes remains uncertain. Objectives This study describes ILN identification, follow-up management, and timeliness of cancer treatment after implementation of an AI-enabled NLP platform at a large health system. Design Single-center retrospective observational study. Methods Adults with ILNs identified on imaging between April 2023 and May 2024 were included. The pre-AI cohort was identified using diagnosis codes, while the post-AI cohort was identified using an NLP-based AI platform. Because these methods captured different populations, no formal comparative testing was performed, and outcomes were summarized descriptively. Results A total of 1,098 patients with ILNs were identified: 228 from 6,828 pre-AI imaging studies (3.3%) and 870 from 9,902 post-AI imaging studies (8.8%). Most pre-AI ILNs were identified on outpatient imaging (74%), while post-AI ILNs were identified in inpatient (35%), emergency department (26%), and outpatient (39%) settings. A follow-up recommendation was documented for 195 of 228 pre-AI patients (85.5%) and 563 of 870 post-AI patients (64.7%). Among these patients, completion was documented for 167 of 195 (85.6%) and 414 of 563 (73.5%), respectively. Malignancy was documented in 28 of 228 pre-AI patients (12.3%) and 15 of 870 post-AI patients (1.7%). Median imaging-to-biopsy time was 78 days pre-AI and 120 days post-AI; biopsy-to-treatment and imaging-to-treatment intervals were similar. Conclusion The post-AI cohort included more identified ILNs and a greater proportion from acute-care settings than the historical pre-AI cohort. Because identification methods were not comparable, observed differences in documented malignancy diagnosis and treatment timing cannot be attributed to the platform. Effective implementation requires defined clinical ownership and follow-up infrastructure.
SN - 1753-4666
SN - 1753-4658
ER -