Non-small cell lung cancer ( NSCLC ) is one of the most prevalent and deadly malignant tumors in the world. In recent years, artificial intelligence (AI) in computed tomography (CT) has harnessed the power of big data to automatically extract and learn imaging features, thereby assisting radiologists in reducing the workload and missed diagnosis rate of pulmonary nodules. An ELISA kit for detecting seven lung cancer autoantibodies (p53, SOX2, PGP9.5, CAGE, MAGE-A1, GAGE7, and GBU4-5) has been clinically implemented in China, showing high specificity in the early screening of NSCLC. Additionally, other liquid biopsy techniques such as circulating tumor DNA (ctDNA) methylation markers are also continually being explored. However, existing methods for the early diagnosis of lung cancer all have their limitations, and optimizing their combination or establishing diagnostic models has become a trend. This review summarizes the research progress and value of the seven lung cancer autoantibodies and CT AI in the early diagnosis of NSCLC, with the aim of providing a reference for their combined use in the early diagnosis of lung cancer in Chinese population.