Background <p>The growing demand for follow-up imaging highlights the need for tools supporting the assessment of pulmonary nodules over time. We evaluated the performance of an artificial intelligence (AI)-based system for automated nodule matching.</p> Methods <p>In this single-center study, patients with nodules and ≤ 2 chest computed tomography (CT) examinations were retrospectively selected. An AI-based algorithm was used for automated nodule detection and matching. The matching rate and the causes for incorrect matching were evaluated for the ten largest lesions (5–30 mm in diameter) registered on baseline CT. The dependence of the matching rate on nodule number and localization was also analyzed.</p> Results <p>One hundred patients (46 females), with a median age of 62 years (interquartile range 57–69), and 253 CTs were included. Focusing on the ten largest lesions, 1,141 lesions were identified, of which 36 (3.2%) were other structures incorrectly identified as nodules (false-positives). Of the 1,105 identified nodules, 964 (87.2%) were correctly detected and matched. The matching rate for nodules registered in both baseline and follow-up scans was 97.8%. The matching rate per case ranged 80.0–100.0% (median 90.0%). Correct matching rate decreased in follow-up examinations to over 50 nodules (<i>p</i> = 0.003), with an overrepresentation of missed matching. Matching rates were higher in parenchymal (91.8%), peripheral (84.4%), and juxtavascular (82.4%) nodules than in juxtaphrenic nodules (71.1%) (<i>p</i> &lt; 0.001). Missed matching was overrepresented in juxtavascular, and incorrect assignment in juxtaphrenic nodules.</p> Conclusion <p>The correct automated-matching rate of metastatic pulmonary nodules in follow-up examinations was high, but it depends on localization and a number of nodules.</p> Relevance statement <p>The algorithm enables precise follow-up matching of pulmonary nodules, potentially providing a solid basis for standardized and accurate evaluations. Understanding the algorithm’s strengths and weaknesses based on nodule localization and number enhances the interpretation of AI-based results.</p> Key Points <p><UnorderedList Mark="Bullet"> <ItemContent> <p>The AI algorithm achieved a correct nodule matching rate of 87.2% and up to 97.8% when considering nodules detected in both baseline and follow-up scans.</p> </ItemContent> <ItemContent> <p>Matching accuracy depended on nodule number and localization.</p> </ItemContent> <ItemContent> <p>This algorithm has the potential to support response evaluation criteria in solid tumor-based evaluations in clinical practice.</p> </ItemContent> </UnorderedList></p> Graphical Abstract <p></p>