Development and application of an intelligent assessment system for medical clinical skill training

The digital transformation of medical education represents a significant global development trend. However, clinical skill assessment, a vital method to inspect education outcome, has historically depended on conventional manual evaluation methods. To further explore intelligent training paradigms and realize autonomous instruction and automated assessment, this study introduces contrastive learning methods to construct a novel clinical skill assessment system characterized by unified standard, visible process, and objective evaluation. Practical validation indicates that the model effectively differentiates varying levels of proficiency in the specific surgical procedures. Under the optimal parameter combination, the prediction of one operational procedure achieved a peak accuracy of 94.01%. With a one-point tolerance, accuracy further improved to 96.41%. Overall, depending on its objectivity and standardization, the model significantly enhances the scientific rigor and efficiency of the clinical skill training process, while also providing immediate feedback on performance.