OBJECTIVE: To quantitatively evaluate the accuracy of data obtained from liquid-interference surfaces using an intraoral 3D scanner (IOS) integrated with a compressed airflow system, so as to provide clinical proof of accuracy for the application of the compressed airflow system-based scanning head in improving data quality on liquid-interference surfaces. METHODS: The study selected a standard model as the scanning object, adhering to the "YY/T 1818-2022 Dental Science Intraoral Digital Impression Scanner" guidelines, a standard that defined parameters for intraoral scanning. To establish a baseline for accuracy, the ATOS Q 12M scanner, known for its high precision, was used to generate true reference values. These true values served as the benchmark for evaluating the IOS performance. Building on the design of an existing scanner, a new scanning head was developed to integrate with a compressed airflow system. This new design aimed to help the IOS capture high-precision data on surfaces where liquid-interference, such as saliva, might otherwise degrade scanning accuracy. The traditional scanning method, without airflow assistance, was employed as a control group for comparison. The study included five groups in total, one control group and four experimental groups, to investigate the effects of scanning lens obstruction, airflow presence, liquid media, and the use of the new scanning head on scanning process and accuracy. Each group underwent 15 scans, generating ample data for a robust statistical comparison. By evaluating trueness and precision in each group, the study assessed the impact of the compressed airflow system on the accuracy of IOS data collected from liquid-interference surfaces. Additionally, we selected Elite and Primescan scanners as references for numerical accuracy values. RESULTS: The scanning accuracy on liquid-interference surfaces was significantly reduced in terms of both trueness and precision [Trueness: 18.5 (6.5) CONCLUSION: The scanning head based on the compressed airflow system can assist in improving the accuracy of data obtained from liquid-interference surfaces by the IOS.