Abstract:ObjectiveTo evaluate the perfomance of ChatCPT-4.0 and ERNE Bot-4.0 in the field of laloratory medlicine , and ex-plore their application potential and challenges in this pofessional domain.Methods Using the national clinical medical laboratorytechnology ( intermedliale) examination questions as a benchmark, we compared the performance of the two models in mastering labora-tory medlicine knowledge and answering consistency.We also and assessed the molels’ ability in inferpreting test results and assistingdiagnosis through 30 laboratory medicine cases.Results In the clinical medlical examination technology test， both modlels passed the60% qualification threshold.ChatCPT-4.0 was superior to ERNE Bot-4.0 in terms of answering speed and consistency, but its answer-ing accuracy was significantly lower than that of ERNE Bot-4.0 (73.25% vs 80.75%). ERNE Bot-4.0's accuracy rate was higher thanthe average accturacy rate of clinical aboratory personnel in this examination(78.03% ). In the accuracy analysis of different questiontypes , both performed wost in experimental techmology questions ( ERNE Bot-4.0:66.32% , ChatGPT-4.0 :60.53%）) and best in bas-ic medical knowledge questions( both scoring 86.00% ) . In the case analysis test,ERNE Bot-4.0 outperformed ChatCPT-4.0 in all cat-egories.Both molels performed well in routine case analysis but made erros in complex case analysis. Conclusion In the field of la-boratory medicine，both large language modlels have shown certain application potential , especially in a Chinese context , where ERNIEBot-4.0 significantly outperforms ChatCPT-4.0 in terms of answering accuracy and case analysis ability , indlicating its relative adlvantagein domestic applications.However, both models still need improvement in experimental technical knowledge , complex case analysis ca-pabilities, and the accuracy and consistency of result output. At the current stage , there are still certain risks in directly applying suchgeneral large language models to clinical test result interpretation and assisted diagnosis , which provides a new research direction for theinterpretation of test reports.