A lung nodule is a small (< 30 millimetres), well defined lesion completely surrounded by pulmonary parenchyma (i.e., functional tissue of the lung). Lung nodules are classified as solid or subsolid, and subsolid nodules are subdivided into pure ground-glass nodules (no solid component) and part-solid nodules (both ground glass and solid components). A lesion that measures over 30 millimetres is considered a lung mass. An important distinction for the patient and treatment plan is whether the presenting lung nodule(s) are benign or malignant. For lung nodules, this appropriate classification is crucial to prevent any unnecessary procedures as well as for appropriate treatment planning (e.g., biopsy, surgical resection). It has been found that the majority of lung nodules identified on computed tomography (CT) scans are benign with a prevalence of malignancy as low as one percent for Canadians with lung nodules. To discern whether a lung nodule is benign or malignant, the initial evaluation usually involves a radiologist using clinical and radiographic features (often from a CT scan) to determine the likelihood of malignancy; this likelihood assists in determining further management (e.g., CT surveillance, biopsy). However, discerning malignancy from clinical and radiographic features can be challenging and novel methods are being considered, including artificial intelligence (AI). AI is a branch of computer science concerned with the development of systems that can perform tasks that would usually require human intelligence, such as problem-solving, reasoning, and recognition. AI is an umbrella term that includes a number of subfields and approaches. AI algorithms for reading CT scans often include a machine learning system (e.g., support vector machine [SVM], artificial neural networks [deep learning, including convolutional neural network or CNN]). Machine learning involves training an algorithm to perform tasks by learning from patterns in data rather than performing a task that it is explicitly programmed to do. In order to train the machine learning program, data are divided into learning sets (i.e., human indicates if an outcome of interest is present or absent) and validation sets (i.e., system used what it learns to indicate if the outcome of interest is present or absent). CADTH has previously reviewed the evidence for the use of AI for nodule classification in screening, incidental identification, or known or suspected malignancies for lung cancer via a Rapid Response Summary of Abstracts. The aim of the current report is to summarize and critically appraise the evidence initially identified in the Summary of Abstracts, based on additional screening and review of the full text of these publications.