Chinese Journal of Scientific and Technical Periodicals

Comparative effectiveness of AIGC detection tools in identifying AI⁃generated content within medical review abstracts

WANG Haijuan, SHEN Xibin, FEI Xiuyun, FU Hui, LI Peng, ZHAO Wei

Chinese Journal of Scientific and Technical Periodicals, 2026, 37(1): 24-31. DOI: 10.11946/cjstp.202512261632

检测工具	原文本组（n=518）		AI润色组（n=518）		AI写作组（n=518）		P值
检测工具	均数或中位数	标准差或四分位数	均数或中位数	标准差或四分位数	均数或中位数	标准差或四分位数	P值
iThenticate	0.101	0.015	0.099	0.013	0.110	0.012	< 0.001
万方AIGC检测	0	0， 0.795	0.797	0， 0.805	0.803	0.790， 0.809	< 0.001
知网AIGC检测^1）	1	1， 1	2	1， 2	3	2， 3	< 0.001
鉴字源AIGC检测	0.007	0， 0.057	0.738	0.477， 0.797	0.800	0.800， 0.800	< 0.001

Table 2 Descriptive statistics of detection scores from 4 AIGC detection tools across text groups

Other figure/table from this article

Figure 1 Distribution of abstract lengths in review articles published from 2018 to 2022
Table 1 Comparison of usage conditions and results presentation across 4 AIGC detection tools
Table 3 Comparison of annotation accuracy among 4 AIGC detection tools
Figure 2 Analysis of consistency among the detection results of 3 domestically developed AIGC detection tools
Table 4 Sensitivity and specificity analysis of 4 AIGC detection tools