Apr 23, 2024
As concerns over research integrity grow, AI-powered tools are becoming essential for detecting image manipulation and duplication in scientific publications. Many errors stem from unintentional mistakes, but fraudulent practices, such as paper mills, further threaten trust in scientific literature. Automated solutions like Proofig AI help researchers, institutions, and publishers identify issues early, preventing costly retractions and strengthening the credibility of scientific findings.
The prevalence of research integrity issues has become a growing concern in the academic publishing community, particularly in the life sciences. Several studies suggest that the frequency of image-related issues is increasing, which, over time, could reduce the credibility of scientific literature, lead to costly investigations, and cause reputational damage. As high profile cases about image integrity problems in scientific papers become more frequent, the community must consider how to overcome the issues with the manual image review process and the benefits of AI in rapidly detecting, and potentially preventing, these issues.
The US Office for Research Integrity (ORI), for example, has previously noted a significant increase in the number of allegations involving questionable scientific images. Between 2007 and 2008, the ORI reported that 68 percent of all the cases it opened regarding research misconduct involved image manipulation, compared with approximately 2.5 percent in the period of 1989 to 1990.
This issue is still ongoing, with Nature reporting in December 2023 that the number of retractions issued for research articles in 2023 surpassed 10,000. According to Jana Christopher, an image data integrity analyst at FEBS Press, the percentage of manuscripts flagged for image-related problems in manuscripts ready for publication ranges from 20 to 35 percent. This statement, derived from expert interviews conducted by the UK Research Integrity Office (UKRIO), suggests a significant concern in the life-sciences community. These findings by Jana Christopher are supported by the statistical data from Proofig AI, which specializes in detecting duplications and manipulations in scientific images.
The rising percentages indicate that more researchers may be at risk for been reported for potential image issues within some of their publications. If these issues are reported post-publication, publishers and research integrity officers may need to investigate, which can take months or even years. No matter the result, researchers, their institutions and the publishers involved will often face reputational damage because of the accusations. This could limit a researcher’s, and sometimes even the institution’s, ability to gain funding in the future, while lowering the perception of quality for the journal that initially published the piece.
Image intentions
Understandably, researchers are often fearful of the reputational damage associated with allegations of fraud or retractions of their paper. On the other hand, others may believe that it’ll never happen to them, because they know they are not including fraudulent information. However, when looking more closely at allegations or investigations over time, only a small portion of issues can be categorized as intentional manipulation.
For example, the AACR conducted a trial that ran from January 2021 to May 2022, where the team used Proofig AI to screen 1,367 papers accepted for publication. Of the papers reviewed, 207 papers required author contact to clear up issues such as mistaken duplications, and only four papers were withdrawn. In almost all cases (204 cases), there was no evidence of intentional image manipulation, but rather the problems were simply honest mistakes.
This suggests that all researchers should consider how to maintain image integrity and how to uncover any potential issues before publication. So what should principal investigators and editors do to find all these mistakes prior to publication (and ideally, before submission)?
How issues occur
Image issues can occur for several reasons, whether it’s accidentally mislabeling images, or overlapping sections of a sample when capturing them. These intricate details are incredibly difficult to find by eye, particularly when researchers and editors must review hundreds of similar images, which often results in hundreds of thousands of comparisons between subimages.
In life science papers, microscopy images are typically the source of detected image issues, and duplications commonly occur during experimentation. While capturing specimen images, researchers move the microscope across the slide to document each section. The microscope will not alert the user if there are any overlaps as they capture images and if the researcher regularly changes the magnification, they may inadvertently duplicate specific sample areas. Details in the figures are very intricate, so it will be difficult to detect these overlaps by eye.
These types of errors should not be confused with deliberate attempts to mislead, but could lead to accusations about the validity of the research. It serves all involved to head off these human errors prior to publication.
The problem with papermills
While honest mistakes make up some portion of image integrity issues in the research literature, instances of fraud are also on the rise, particularly with the emergence of papermills. These services create fraudulent content resembling academic papers, which are often difficult to identify and therefore regularly published.