Objective To explore the performance of domestic and international large language models(LLMs)in the context of ques-tion banks for clinical examination knowledge.Methods The performance of six domestic or international LLMs,in the question banks with a set of 330 questions for intermediate-level of clinical medical laboratory technology were assessed.The differences in accuracy and consistency among the different LLMs were evaluated using chi-square tests,Fisher's exact tests and logistic regression.Results The accuracy results for the four English LLMs along with 95％confidence intervals(95％CI)were as follows:the accuracy rates of ChatGPT,BingAI,Claude and GPT-4 were demonstrated as 0.56(95％CI:0.527-0.601),0.61(95％CI:0.572-0.644),0.64(95％CI:0.607-0.678)and 0.80(95％CI:0.767-0.833)respectively,while the performance of Xinghuo and Tiangong yielded accuracy rates of 0.52(95％CI:0.479-0.561)and 0.45(95％CI:0.408-0.482)respectively.Using ChatGPT as the reference model,we found that the odds ratios(OR)of correct answers of BingAI,Claude and GPT-4 were 1.272(95％CI:1.020-1.588),1.397(95％CI:1.119-1.743)and 3.270(95％CI:1.904-2.729)respectively.The differences of LLMs performance were statistically significant(P＜0.05)for all the three models.In terms of consistency,Tiangong and BingAI showed poor consistency,while GPT-4 appeared better.Conclusion A-mong the six LLMs,GPT-4 demonstrated the highest overall accuracy and consistency in each question category.

Clinicians need to focus on various points in the diagnosis and treatment of insomnia.This article prescribed the treatment protocol based on the unique features,such as insomnia in the elderly,women experiencing specific physiologi-cal periods,children insomnia,insomnia in sleep-breathing disorder patients,insomnia in patients with chronic liver and kidney dysfunction.It pro-vides some reference for clinicians while they make decision on diagnosis,differentiation and treat-ment methods.