PloS one
Real-world before-and-after evaluation of AI support for lung cancer diagnosis at three US lung nodule clinics
By Lee KA, Borah A, Fiscus G and 6 others.
Abstract
Background
Health 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 methods
A 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.