The journal editors first screen the articles for efficiency and effectiveness in the review process using AI tools. AI detects plagiarism, grammatical and linguistic errors, and adherence to guidelines. It also can analyze structure, relevance, and novelty of the research by comparing with the existing literature. Further, the AI-powered tools help them to identify potential ethical issues, check citations, and even forecast the chances of acceptance based on trends seen earlier. Automation of these pre-checks by AI frees editors to give their services to more in-depth content evaluation and peer review, making the screening process faster and efficient.

Jeeva Balu

Shahnawaz Mohammed - Dear Sir, Thank you for your response. Could you recommend any AI tool to detect plagiarism, grammatical, linguistic errors, structure , relevance, check citations from paper etc.

Dear Sir, thank you for your inquiry. For checking plagiarism, tools like Turnitin or Copyscape are quite effective. For grammar and linguistic checks, Grammarly and ProWritingAid are great tools. For checking paper structure and relevance checks, as well as citation checks, tools like PaperRater or Scribbr can be used, which also offer citation checks. These tools can be used to ensure the quality and originality of your work in numerous ways.

Jeeva Balu

AI detectors are poor western blot classifiers: a study of accuracy and predictive values

"The recent rise of generative artificial intelligence (AI) capable of creating scientific images presents a challenge in the fight against academic fraud. This study evaluates the efficacy of three free web-based AI detectors in identifying AI-generated images of western blots, which is a very common technique in biology. We tested these detectors on AI-generated western blot images (n = 48, created using ChatGPT 4) and on authentic western blots (n = 48, from articles published before the rise of generative AI). Each detector returned a very different sensitivity (Is It AI?: 0.9583; Hive Moderation: 0.1875; and Illuminarty: 0.7083) and specificity (Is It AI?: 0.5417; Hive Moderation: 0.8750; and Illuminarty: 0.4167), and the predicted positive predictive value (PPV) for each was low. This suggests significant challenges in confidently determining image authenticity based solely on the current free AI detectors. Reducing the size of western blots reduced the sensitivity, increased the specificity, and did not markedly affect the accuracy of the three detectors, and only slightly improved the PPV of one detector (Is It AI?). These findings highlight the risks of relying on generic, freely available detectors that lack sufficient reliability, and demonstrate the urgent need for more robust detectors that are specifically trained on scientific contents such as western blot images."

https://peerj.com/articles/18988/

Identifying AI-Generated Research Papers: Methods and Considerations

"Recent advancements in natural language generation (NLG) have revolutionized content creation, enabling artificial intelligence (AI) tools to produce coherent and seemingly authentic texts, including scholarly papers. While AI-generated content offers efficiencies in speed and volume, concerns over authenticity, ethical implications, and academic integrity persist. This review explores methods and considerations for identifying AI-generated research papers, emphasizing the need to distinguish between human-authored and AI-generated content to uphold scholarly standards and ensure transparency in research. Key detection techniques include textual analysis, metadata examination, and content evaluation. Ethical concerns regarding AI's role in research are also discussed, underscoring the importance of ongoing research to refine identification methodologies and maintain research integrity."

Article Identifying AI-Generated Research Papers: Methods and Considerations