Feasibility and ethical considerations of ChatGPT focusing on research hotspots in clinical medical education management
10.12026/j.issn.1001-8565.2024.09.14
- VernacularTitle:ChatGPT聚焦临床医学教育管理研究热点的可行性与伦理考量
- Author:
Jialin ZENG
1
;
Ping SU
;
Fangwan HUANG
Author Information
1. 福建医科大学临床医学部,福建 福州 350108
- Keywords:
clinical medical education management;
research hotspot;
large-scale language model;
ethical risk;
evasive route
- From:
Chinese Medical Ethics
2024;37(9):1108-1118
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the feasibility of using the large-scale language model ChatGPT to focus on research hotspots in the field of clinical medical education management,aiming to accelerate the scientific research process in this field.Methods:First,six key topics in the field were selected,and ChatGPT was guided to automatically generate the five most urgent or important research hotspots in each topic through questioning.Then,six clinical medical education managers were organized to use the five-point Likert scale to evaluate the research hotspots generated by ChatGPT from five dimensions,including pertinence,humanity,dialectics,expansion,and originality.Finally,the evaluation results were analyzed from multiple perspectives based on descriptive statistics,score similarity,and indicator correlation.Results:The research hotspots generated by ChatGPT performed excellently in terms of topic specificity,and were also satisfactory in terms of humanistic,dialectical,and expansive aspects,but performed mediocrely in terms of originality.Conclusion:ChatGPT can serve as an auxiliary tool to focus on research hotspots in clinical medical education management,but more efforts are still needed to enhance the originality of the research hotspots it generates.However,when using ChatGPT to focus on research hotspots,there are a series of ethical risks,including false and abusive data,discrimination and bias in algorithms,as well as academic dishonesty and misconduct.From the technical perspective,researchers can integrate the focused results of multiple large-scale language models,and utilize data and algorithm diversity to avoid ethical risks.From the application perspective,individual identification,group argumentation,and other means can be utilized,and carefully examined with critical thinking to avoid ethical risks.
