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ASM采用Imagetwin解决图像重复问题并保持科学准确性

Aashi P Chaturvedi¹, Alicea Hibbard¹, Chad Nelson¹

¹Department of Ethics and Integrity, Journals, American Society for Microbiology, Washington, DC, USA.

|August 25, 2025

概括

科学图像的重复破坏了人们的信任. 一项由ASM期刊使用ImagetwinAI的试点研究发现并解决了3.9%的手稿中的图像重复,提高了研究完整性.

科学领域:

科学出版
研究诚信
图像分析

背景情况:

无论是意外还是故意,科学文章中的图像重复都会严重破坏对研究,作者,机构和出版商的信任.
这种重复引发了科学严谨性,潜在的不当行为以及同行评审和编辑监督的有效性,从而削弱了对科学记录的信任.
积极检测图像重复对于保持发表的研究可信度至关重要.

研究的目的:

评估将基于人工智能的工具 (Imagetwin) 整合到编辑工作流中以检测图像重复的有效性.
评估提交给ASM期刊的手稿中发现的图像重复的频率和性质.
完善科学出版物中图像完整性的早期和更有效的选过程.

主要方法:

在美国微生物学协会 (ASM) 期刊的编辑工作流程中实施Imagetwin人工智能工具.
在出版之前进行为期一年的试点研究选接受的,符合条件的手稿复制图像.
分析所发现的图像问题类型和解决过程,包括对手稿接受的影响.

主要成果:

在试点研究中,使用Imagetwin选的合格手稿中有3. 9%出现图像重复.
大多数被发现的形象问题是无意的,并通过与作者沟通成功解决.
由于未解决的图像重复问题,对0.23%的选手稿的接受被撤销.

结论:

整合像Imagetwin这样的人工智能工具,
试点研究表明,积极的图像选加强了科学记录的完整性,并加强了常规的伦理检查.
ASM期刊成功改进了其工作流程, 包括图像重复选, 提高了整体研究可信度.

关键词:

人工智能工具图像双胞胎图像重复图像的完整性科学完整性

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