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ChatGPT, a generative pretrained transformer, has garnered global attention and sparked discussions since its introduction on November 30, 2022. However, it has generated controversy within the realms of medical education and scientific research. This paper examines the potential applications, limitations, and strategies for using ChatGPT. ChatGPT offers personalized learning support to medical students through its robust natural language generation capabilities, enabling it to furnish answers. Moreover, it has demonstrated significant use in simulating clinical scenarios, facilitating teaching and learning processes, and revitalizing medical education. Nonetheless, numerous challenges accompany these advancements. In the context of education, it is of paramount importance to prevent excessive reliance on ChatGPT and combat academic plagiarism. Likewise, in the field of medicine, it is vital to guarantee the timeliness, accuracy, and reliability of content generated by ChatGPT. Concurrently, ethical challenges and concerns regarding information security arise. In light of these challenges, this paper proposes targeted strategies for addressing them. First, the risk of overreliance on ChatGPT and academic plagiarism must be mitigated through ideological education, fostering comprehensive competencies, and implementing diverse evaluation criteria. The integration of contemporary pedagogical methodologies in conjunction with the use of ChatGPT serves to enhance the overall quality of medical education. To enhance the professionalism and reliability of the generated content, it is recommended to implement measures to optimize ChatGPT\'s training data professionally and enhance the transparency of the generation process. This ensures that the generated content is aligned with the most recent standards of medical practice. Moreover, the enhancement of value alignment and the establishment of pertinent legislation or codes of practice address ethical concerns, including those pertaining to algorithmic discrimination, the allocation of medical responsibility, privacy, and security. In conclusion, while ChatGPT presents significant potential in medical education, it also encounters various challenges. Through comprehensive research and the implementation of suitable strategies, it is anticipated that ChatGPT\'s positive impact on medical education will be harnessed, laying the groundwork for advancing the discipline and fostering the development of high-caliber medical professionals.

The application of new technologies, such as artificial intelligence (AI), to science affects the way and methodology in which research is conducted. While the responsible use of AI brings many innovations and benefits to science and humanity, its unethical use poses a serious threat to scientific integrity and literature. Even in the absence of malicious use, the Chatbot output itself, as a software application based on AI, carries the risk of containing biases, distortions, irrelevancies, misrepresentations and plagiarism. Therefore, the use of complex AI algorithms raises concerns about bias, transparency and accountability, requiring the development of new ethical rules to protect scientific integrity. Unfortunately, the development and writing of ethical codes cannot keep up with the pace of development and implementation of technology. The main purpose of this narrative review is to inform readers, authors, reviewers and editors about new approaches to publication ethics in the era of AI. It specifically focuses on tips on how to disclose the use of AI in your manuscript, how to avoid publishing entirely AI-generated text, and current standards for retraction.

The primary objective of any research, regardless of its domain such as health, technology, psychology, or any other subject, is to enhance the overall well-being of individuals. Rigorous processes are involved in conducting research ethically and in communicating its outcomes to society. However, as publishing research has become a mandatory requirement for career advancement and appointments, academics are resorting to several unethical practices to get substandard work published quickly. Consequently, predatory publishing markets have emerged, which publish data that is falsified and fabricated, along with plagiarised textual matter. The emergence of \"paper mills\" is a further step in the corruption of research, where a group of persons or automated systems generate papers for publication. Anyone desirous of publishing a paper can purchase one, akin to any desired fast-moving consumer product, with the added guarantee of publication in indexed journals. Therefore, paper mills and their unethical modus operandi are discussed in this paper in detail, with relevant examples. The article unfolds the consequences of publishing such fraudulent research papers and concludes with the challenges in combating paper mills.

BACKGROUND: This study was conducted to assess the knowledge and ongoing practices of plagiarism among the journal editors of Nepal.
METHODS: This web-based questionnaire analytical cross-sectional was conducted among journal editors working across various journals in Nepal. All journal editors from NepJOL-indexed journals in Nepal who provided e-consent were included in the study using a convenience sampling technique. A final set of questionnaires was prepared using Google Forms, including six knowledge questions, three practice questions (with subsets) for authors, and four (with subsets) for editors. These were distributed to journal editors in Nepal via email, Facebook Messenger, Viber, and WhatsApp. Reminders were sent weekly, up to three times. Data analysis was done in R software. Frequencies and percentages were calculated for the demographic variables, correct responses regarding knowledge, and practices related to plagiarism. Independent t-test and one-way ANOVA were used to compare mean knowledge with demographic variables. For all tests, statistical significance was set at p < 0.05.
RESULTS: A total of 147 participants completed the survey.The mean age of the participants was found to be 43.61 ± 8.91 years. Nearly all participants were aware of plagiarism, and most had heard of both Turnitin and iThenticate. Slightly more than three-fourths correctly identified that citation and referencing can avoid plagiarism. The overall mean knowledge score was 5.32 ± 0.99, with no significant differences across demographic variables. As authors, 4% admitted to copying sections of others\' work without acknowledgment and reusing their own published work without proper citations. Just over one-fifth did not use plagiarism detection software when writing research articles. Fewer than half reported that their journals used authentic plagiarism detection software. Four-fifths of them suspected plagiarism in the manuscripts assigned through their journal. Three out of every five participants reported the plagiarism used in the manuscript to the respective authors. Nearly all participants believe every journal must have plagiarism-detection software.
CONCLUSIONS: Although journal editors\' knowledge and practices regarding plagiarism appear to be high, they are still not satisfactory. It is strongly recommended to use authentic plagiarism detection software by the journals and editors should be adequately trained and update their knowledge about it.

UNASSIGNED: The study examines the prevalence of plagiarism in hijacked journals, a category of problematic journals that have proliferated over the past decade.
UNASSIGNED: A quasi-random sample of 936 papers published in 58 hijacked journals that provided free access to their archive as of June 2021 was selected for the analysis. The study utilizes Urkund (Ouriginal) software and manual verification to investigate plagiarism and finds a significant prevalence of plagiarism in hijacked journals.
UNASSIGNED: Out of the analyzed sample papers, 618 (66%) were found to contain instances of plagiarism, and 28% of papers from the sample (n = 259) displayed text similarities of 25% or more. The analysis reveals that a majority of authors originate from developing and ex-Soviet countries, with limited affiliation ties to developed countries and scarce international cooperation in papers submitted to hijacked journals. The absence of rigorous publication requirements, peer review processes, and plagiarism checks in hijacked journals creates an environment where authors can publish texts with a significant amount of plagiarism.
UNASSIGNED: These findings suggest a tendency for fraudulent journals to attract authors who do not uphold scientific integrity principles. The legitimization of papers from hijacked journals in bibliographic databases, along with their citation, poses significant challenges to scientific integrity.

Introduction Plagiarism is appropriating another person\'s ideas, words, results, or processes without giving appropriate credit and usually claiming them to be one\'s own. Thus, plagiarism is a dishonest act of fraud or cheating. Objectives The objective of this study is to assess the perception of plagiarism among medical postgraduate (PG) students. Materials & Methods: An educational observational study was conducted among second-year PG students about the perception of plagiarism by using pre-test and post-test questionnaires after an orientation session on plagiarism and data analysis before the start of dissertation analysis. Questions included were on awareness and attitude towards plagiarism.  Results A survey involving 91 PG students assessed their understanding of plagiarism. Remarkably, the majority (97.7%) demonstrated awareness of plagiarism, yet only 18.6% had authored a published article. It was discovered that about 30% of the students had resorted to plagiarism at some point during their academic pursuits. Approximately 70.9% of the PG students were acquainted with the University\'s plagiarism policy. The survey highlighted a notable enhancement in plagiarism awareness among PG students, with their attitudes toward plagiarism evolving after participating in the session. Conclusion Plagiarism can be avoided by implementing rigorous guidelines, ensuring strict policy adherence, and providing comprehensive training before commencing work. Training, retraining, and strict institute policies will help increase awareness about plagiarism and reduce the percentage of plagiarism in scientific writing.

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Research misconduct refers to deliberate or accidental manipulation or misrepresentation of research data, findings, or processes. It can take many forms, such as fabricating data, plagiarism, or failing to disclose conflicts of interest. Data falsification is a serious problem in the field of medical research, as it can lead to the promotion of false or misleading information. Researchers might engage in p-hacking - the practice of using someone else\'s research results or ideas without giving them proper attribution. Conflict of interest (COI) occurs when an individual\'s personal, financial, or professional interests could potentially influence their judgment or actions in relation to their research. Nondisclosure of COI can be considered research misconduct and can damage the reputation of the authors and institutions. Hypothesis after results are known can lead to the promotion of false or misleading information. Cherry-picking data is the practice of focusing attention on certain data points or results that support a particular hypothesis, while ignoring or downplaying results that do not. Researchers should be transparent about their methods and report their findings honestly and accurately. Research institutions should have clear and stringent policies in place to address scientific misconduct. This knowledge must become widespread, so that researchers and readers understand what approaches to statistical analysis and reporting amount to scientific misconduct. It is imperative that readers and researchers alike are aware of the methods of statistical analysis and reporting that constitute scientific misconduct.

UNASSIGNED: The introduction of ChatGPT by OpenAI has garnered significant attention. Among its capabilities, paraphrasing stands out.
UNASSIGNED: This study aims to investigate the satisfactory levels of plagiarism in the paraphrased text produced by this chatbot.
UNASSIGNED: Three texts of varying lengths were presented to ChatGPT. ChatGPT was then instructed to paraphrase the provided texts using five different prompts. In the subsequent stage of the study, the texts were divided into separate paragraphs, and ChatGPT was requested to paraphrase each paragraph individually. Lastly, in the third stage, ChatGPT was asked to paraphrase the texts it had previously generated.
UNASSIGNED: The average plagiarism rate in the texts generated by ChatGPT was 45% (SD 10%). ChatGPT exhibited a substantial reduction in plagiarism for the provided texts (mean difference -0.51, 95% CI -0.54 to -0.48; P<.001). Furthermore, when comparing the second attempt with the initial attempt, a significant decrease in the plagiarism rate was observed (mean difference -0.06, 95% CI -0.08 to -0.03; P<.001). The number of paragraphs in the texts demonstrated a noteworthy association with the percentage of plagiarism, with texts consisting of a single paragraph exhibiting the lowest plagiarism rate (P<.001).
UNASSIGNED: Although ChatGPT demonstrates a notable reduction of plagiarism within texts, the existing levels of plagiarism remain relatively high. This underscores a crucial caution for researchers when incorporating this chatbot into their work.