Narrative review: the research advances of artificial intelligence in the prediction of pulmonary nodule growth

Background and Objective: Lung cancer is a leading cause of cancer-related mortality worldwide, and dynamic computed tomography (CT) monitoring of pulmonary nodules is central to early detection. However, frequent follow-up scans increase radiation exposure and strain medical resources. This narrative review aims to summarize current artificial intelligence (AI)-based approaches for predicting pulmonary nodule growth on CT, compare model performance, and discuss key challenges and future directions for clinical translation. Methods: We synthesized peer-reviewed evidence on CT-based volumetric, radiomics, radiogenomics, and machine learning (ML) models related to pulmonary nodule growth or growth-related endpoints. Articles were identified through database searches, reference list screening, and guideline consultation. Eligible publications included human original studies, reviews, and meta-analyses. Conference abstracts, editorials, letters, animal or phantom studies were excluded. No language restrictions were applied. Key Content and Findings: Existing AI-based growth prediction studies generally follow three workflows: baseline CT models estimating future growth risk, longitudinal CT models modeling voxel- or pixel-wise growth, and models based growth-related surrogates [such as volume or mass doubling time (MDT), invasiveness, or stage shift]. Across these workflows, AI-driven volumetric and radiomic features detect nodule growth earlier and more consistently than simple diameter measurements and better discriminate fast-growing from indolent nodules. At the same time, emerging explainable AI frameworks help identify influential features, potentially improving trust and adoption. However, practical challenges remain, including picture archiving and communication systems (PACS) integration and balancing sensitivity with specificity, overdiagnosis, and false progression. Most models are retrospective and single-center, use heterogeneous protocols and non-standardized growth definitions, and lack external or prospective validation, limiting generalizability. Conclusions: AI, particularly deep learning (DL) combined with quantitative radiomics and radiogenomics, shows promise for noninvasive prediction of pulmonary nodule growth. Future work should focus on multicenter prospective validation, standardized growth endpoints, low-dose protocols, multimodal data integration, and explainable, federated, generative AI to improve robustness, transparency, and data privacy. In addition, seamless PACS integration and explicit balancing of sensitivity, specificity and overdiagnosis are essential. Ultimately, validated AI models may enable more accurate, personalized surveillance while reducing radiation exposure and resource burden.