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Caroline M. Godfrey; Ashley A. Leech; Kevin C. McGann; Jinyi Zhu; Hannah N. Marmor; Sophia Pena; Lyndsey C. Pickup; Fabien Maldonado; Evan C. Osmundson; Stacie B. Dusetzina; Eric L. Grogan; Stephen A. Deppen (2026).Ìý.ÌýPLOS ONE, 21(3), e0343492.Ìý

Researchers evaluated whether artificial intelligence (AI) could help doctors better judge the cancer risk of indeterminate pulmonary nodules, which are small lung spots seen on a CT scan whose cause is not yet clear. These nodules are becoming more common as lung cancer screening and CT imaging are used more often. The team built a decision model to compare two approaches: clinician evaluation alone versus clinician evaluation supported by AI-based radiomics, a method that analyzes patterns in imaging data. They asked whether the AI approach would improve health outcomes and whether it would be worth the extra cost from a payer’s perspective over a patient’s lifetime. In their base case—a 60-year-old patient with a 1.1 cm nodule and a fairly high chance of cancer (65%)—AI support led to a small gain of 0.03 life-years and was cost-effective, with an incremental cost-effectiveness ratio of $4,485 per life-year gained. However, when the chance that the nodule was cancer was very low, below 5%, the AI approach no longer met a typical cost-effectiveness threshold of $100,000 per life-year gained. Overall, the study suggests that AI-assisted nodule assessment is cost-effective in settings where the likelihood of cancer is greater than 5%.

Fig 1.ÌýDecision Model Structure.

The decision tree structure models the risk-stratification of an indeterminate pulmonary nodule utilizing artificial intelligence-assistance compared to the clinician alone. Repeated portions of the model have been collapsed into subtrees (A-G) for readability, each of which represents a diagnostic or management pathway that appears in various parts of the model (‘A’ = surveillance; ‘B’ = PET-CT evaluation; C = minimally invasive surgical (MIS) lobectomy; ‘D’ = low-risk surveillance; ‘E’ = intermediate-risk; ‘F’ = MIS wedge resection; ‘G’ = initial risk classification).