Narrative review of the application of artificial intelligence-related technologies in the diagnosis of pulmonary nodules with recommendations for clinical practice and future research.

BACKGROUND AND OBJECTIVE

Lung cancer has the highest global incidence and mortality rate among malignancies, primarily due to the complexity and diversity of early lung nodules and tumor changes, in addition to the involvement of numerous other factors, such as delayed screening for early lung nodules and difficulties in treatment. With the continuous optimization of artificial intelligence (AI) models, novel paradigms are emerging in the detection of malignant pulmonary nodules, histopathologic subtyping, and genomic prediction-advances that increasingly inform clinical assessment and therapeutic decision-making. This article reviews Chinese and English research over the past 10 years and explores the current application status of AI in the diagnosis of pulmonary nodules.

METHODS

Literature available primarily from the PubMed, Web of Science, Cochrane Library, and China National Knowledge Infrastructure (CNKI) databases was collected and analyzed to characterize the current state of AI application in the diagnosis of pulmonary nodules across different disciplines. The following keywords were used in the search: "lung cancer", "ground-glass nodules (GGN)", "artificial intelligence", "deep learning", "machine learning", "radiomics", "pathology of lung cancer", and "genomics". The search is limited to English and Chinese publications within the last 10 years, with no restrictions on the age or country of the participants.

KEY CONTENT AND FINDINGS

This study reviews the application of AI in pulmonary nodule diagnosis across imaging, pathology, and genetics. Recently, AI technology has found extensive applications in the healthcare sector. AI significantly enhances the accuracy of early diagnosis and the efficiency of individualized decision-making. However, the standardization of models is insufficient, external validation is scarce, and large-scale prospective studies are still needed to support clinical applications.

CONCLUSIONS

AI is expected to play a significant role in the diagnosis, radiomics, pathology and genomics of pulmonary nodules in the future. Existing evidence can support the differentiation between benign and malignant pulmonary nodules, improve diagnostic efficiency, and predict pathological types and gene mutations. Thus, AI has a promising application prospect in clinical practice.