Research on multi-user collaboration and real-time feedback mechanism of the translation system between ancient Chinese and modern medical terms in Huangdi Neijing based on cloud computing collaborative platform

The present study aims to create a feature-integrated, web-based, multiperson collaborative working environment where real-time feedback mechanisms and artificial intelligence can be used to improve the translation efficiency and accuracy when transferring the ancient Chinese medical knowledge to the contemporary context. Main projects include: multi-user cooperation and real-time feedback of the translation system of ancient Chinese and modern medical terms in Huangdi Neijing by using cloud computing collaboration. The aim of this study is to integrated multi-user/collaborative translation system for translating the archaic Chinese medical terminology used in Huangdi Neijing into modern medical terms using cloud computing environment. In total, 50 participants were engaged in the study, and equally distributed by domain expertise. The use of role-based access means that each of the participants could carry out their activities in the group. The results indicated that the accuracy of the translated contents and sentiments of sentences and phrases were untainted, the collaboration between evaluators was seamless, and overall user satisfaction was high, and so all the collected and measured data was described through statistic analysis and inferential tests. AI-generated translations scored a mean accuracy of 89.5% while the accuracy of expert human translators averaged 94.3% with a p < 0.05 (p = 0.021). The group tasks supported by AI software were accomplished in a shorter time, agreeing on a plan in approximately 12.5 minutes, while the human only tasks took 18.7 minutes, (p = 0.045). More than half of the respondents, (56%) expressed high satisfaction with the real-time feedback system, with the mean user satisfaction score being 4.7 (p=0.035) and majority categorized as “Very Satisfied”. Proposals coming from AI and users, respectively received 75.4% and 85.9% acceptance, the results demonstrating the superiority of human input marking a statistically significant differentiation at p = 0.012). Therefore, the present research established a novel cloud-based multi-user collaborative platform that used AI to translate ancient Chinese medical texts into the modern context. Despite that, AI helped in the work with high accuracy and speed, however the intellectual work in the form of perfect and detailed interpretation of the complex contexts remained the exclusive prerogative of a human translator.