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Objectives: Paper mills, companies that write scientific papers and gain acceptance for them, then sell authorships of these papers, present a key challenge in medicine and other healthcare fields. This challenge is becoming more acute with artificial intelligence (AI), where AI writes the manuscripts and then the paper mills sell the authorships of these papers. The aim of the current research is to provide a method for detecting fake papers., Methods: The method reported in this article uses a machine learning approach to create decision trees to identify fake papers. The data were collected from Web of Science and multiple journals in various fields., Results: The article presents a method to identify fake papers based on the results of decision trees. Use of this method in a case study indicated its effectiveness in identifying a fake paper., Conclusions: This method to identify fake papers is applicable for authors, editors, and publishers across fields to investigate a single paper or to conduct an analysis of a group of manuscripts. Clinicians and others can use this method to evaluate articles they find in a search to ensure they are not fake articles and instead report actual research that was peer reviewed prior to publication in a journal., (© 2023 Walter de Gruyter GmbH, Berlin/Boston.)

At the moment, the academic world is faced with various challenges that negatively impact science integrity. One is hijacked journals, a second, inauthentic website for indexed legitimate journals, managed by cybercriminals. These journals publish any manuscript by charging authors and pose a risk to scientific integrity. This piece compares a journal's original and hijacked versions regarding authority in search engines. A list of 16 medical journals, along with their hijacked versions, has been collected. The MOZ Domain Authority has been used to check the authority of both original and hijacked journals, and the results have been discussed. It indicates that hijacked journals are gaining more credibility than original ones. This should alarm academia and highlights a need for serious action against hijacked journals. The related policies should be planned, and tools should be developed to support easy detection of hijacked journals. On the publishers' side, the visibility of journals' websites must be enhanced to address this issue. [ABSTRACT FROM AUTHOR]

Purpose: Academic and other researchers have limited tools with which to address the current proliferation of predatory and hijacked journals. These journals can have negative effects on science, research funding, and the dissemination of information. As most predatory and hijacked journals are not error free, this study used ChatGPT, an artificial intelligence (AI) technology tool, to conduct an evaluation of journal quality. Methods: Predatory and hijacked journals were analyzed for reliability using ChatGPT, and the reliability of result have been discussed. Results: It shows that ChatGPT is an unreliable tool for journal quality evaluation for both hijacked and predatory journals. Conclusion: To show how to address this gap, an early trial version of Journal Checker Chatbot has been developed and is discussed as an alternative chatbot that can assist researchers in detecting hijacked journals. [ABSTRACT FROM AUTHOR]