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检测			AI润色组AIGC检测			AI写作组AIGC检测
检测工具	阴性/篇	阳性/篇	特异度/%	阴性/篇	阳性/篇	灵敏度/%	阴性/篇	阳性/篇	灵敏度/%
iThenticate	518	0	100.00	518	0	0	518	0	0
万方AIGC检测	365	153	70.46	199	319	61.58	86	432	83.40
知网AIGC检测	476	42	91.89	209	309	59.65	57	461	89.00
鉴字源AIGC检测	512	6	98.84	215	303	58.49	1	517	99.81

Table 4 Sensitivity and specificity analysis of 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
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