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AIGC				AI润色组AIGC				AI写作组AIGC
检测工具	低或无风险/篇	中风险/篇	高风险/篇	准确率/%	低或无风险/篇	中风险/篇	高风险/篇	准确率/%	低或无风险/篇	中风险/篇	高风险/篇	准确率/%
iThenticate	518	0	0	100.00	518	0	0	0	518	0	0	0
万方AIGC检测	365	153	0	70.46	199	319	0	61.58	86	432	0	0
知网AIGC检测	476	17	25	91.89	209	198	111	38.22	57	188	273	52.70
鉴字源AIGC检测	512	1	5	98.84	215	26	277	5.02	1	0	517	99.81

Table 3 Comparison of annotation accuracy among 4 AIGC detection tools

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