Comparative Study on Response Efficacy of Generative Artificial Intelligence for Elderly Diabetes Mellitus

Corresponding author: LIU HAIXIA, 742631571@qq.com

DOI: 10.12201/bmr.202503.00019

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  Abstract: Objective/significance To evaluate the response accuracy of different generative artificial intelligence (GAI) chat robots to the common problems of elderly diabetes, so as to compare the performance differences of various AI large-scale language models in the quality of medical information service. Methods/Process A standardized assessment question bank containing 10 elderly diabetes related questions was constructed, and then four GAI chat robots were used to score the accuracy of the answers. In addition, the problem is summarized into two dimensions of "diagnosis and evaluation" and "control and treatment", and the situation of the above four GAI in these two dimensions is analyzed. Results/Conclusion In general, Kimi and Doubao AI have significantly better response performance than DeepSeek and iFLYTEK spark AI for common problems of elderly diabetes, with higher accuracy and stability, but there is no significant difference in response performance between Kimi and Doubao AI. In addition, in the dimensions of "diagnosis and evaluation", "control and treatment", kimi and Doubao AI has better performance than DeepSeek and iFLYTEK spark AI.

  Key words: generative artificial intelligence; chat robot; large language model; senile diabetes; medical informatics

  Submit time: 26 May 2025

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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