The use of artificial intelligence (AI) in scientific circles has grown significantly over the past decade.1 AI is actively used to analyze large data sets, automate computational processes, and support decision-making in many fields of science.234 In the context of academic publishing and peer review, AI is already showing great potential, offering new opportunities for automated text processing, plagiarism detection, and format checking.5

At the same time, integrating AI into the academic process creates new ethical and methodological challenges, especially in the peer review process.6 The traditional peer review model is based on experts’ deep knowledge and critical thinking in specific fields. The use of AI algorithms for preliminary or automated review raises many questions about such systems’ accuracy, objectivity and ability to assess the scientific novelty and quality of research adequately.7

Therefore, there is a need to actively discuss the possibilities and limitations of AI in the publishing and review process of scientific works. Examining the potential benefits and risks will provide a better understanding of how AI can be effectively integrated into the academic review system without losing the fundamental principles of scientific ethics and quality.

This review aims to evaluate the perspectives of the scientific community on integrating AI tools in the peer review process, focusing on their applications, limitations, and ethical considerations. This study seeks to bridge the gap between the technological advancements offered by AI and the foundational principles of academic publishing, such as transparency, integrity, and objectivity. By identifying best practices, the review aims to provide actionable and evidence-based recommendations for effectively incorporating AI into peer review workflows to maintain the quality and fairness of the process.

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We conducted our literature search on November 21, 2024, using the Scopus, MEDLINE/PubMed, and DOAJ databases, focusing on English-language articles relevant to AI in the context of academic publishing and peer review. This search aimed to identify studies addressing AI’s applications, benefits, limitations, and ethical implications in peer review processes. We utilized combinations of terms such as:

The search terms were strategically chosen based on a preliminary review of relevant literature and refined through iterative searches to encompass broad aspects of AI applications in peer review.

In the review, we included peer-reviewed journal articles, systematic reviews, and editorials specifically discussing AI applications in academic publishing and peer review or highlighting ethical guidelines for AI use. Materials such as book chapters, conference papers, retracted articles, and duplications were excluded, along with the articles focused solely on unrelated AI applications or without an abstract. Two researchers independently assessed eligibility. In cases of disagreement, a third researcher was consulted to reach a consensus. Articles meeting the inclusion criteria were evaluated with consideration for the document type, journal source, citation frequency, and publication relevance. After applying the inclusion criteria, we selected studies that provided valuable insights into the role of AI in streamlining peer review, maintaining ethical standards, and understanding potential limitations. This approach allowed us to compile a comprehensive and balanced literature list to evaluate AI’s role in enhancing academic publishing integrity.

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While ChatGPT and other AI tools are highly effective at language processing and handling general tasks,8 they lack the in-depth subject-matter expertise required to understand or critically evaluate complex scientific content fully. Scientific peer review often requires a deep understanding of specialized subjects, complex methodologies, and cutting-edge discoveries. AI may miss subtle methodological flaws or theoretical inconsistencies because it cannot “reason” through content like an expert. This limitation is particularly critical in disciplines where a thorough understanding of the domain is essential to assess the validity and implications of the research.

One potential risk of incorporating AI into the peer review process is the over-reliance on its capabilities. If editors and reviewers become too dependent on AI-generated suggestions, the quality of peer review could decline.9 AI tools like ChatGPT can offer support but need more meticulous judgment and critical thinking that human reviewers bring to the process. Over-reliance on AI might result in missed errors or inadequate assessments of crucial elements of the manuscript, particularly in areas where human expertise is essential.10 Reviewers must critically evaluate AI-generated content to ensure the final decisions reflect accurate, careful analysis.

One of the most critical aspects of the peer review process is evaluating the novelty and significance of the research. AI tools are limited in assessing these factors.11 They can analyze existing patterns in the literature and detect similarities with previous work. However, they may not fully acknowledge the importance of groundbreaking findings or the value of new theoretical approaches.11 With their deep knowledge of the field and insight into its development, human reviewers are better equipped to make these evaluations. AI’s inability to recognize the potential long-term impact of research is a significant limitation in academic publishing.

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AI models are trained on vast datasets, and the quality of their outputs is directly influenced by the data they have been exposed to. If these datasets contain inherent biases (whether related to gender, race, geographic region, or publication trends) AI tools may inadvertently perpetuate those biases in their suggestions or assessments.12 For example, if a model is disproportionately trained on research from Western institutions, it may overlook valuable contributions from less represented regions. This introduces a risk to the objectivity and fairness of the peer review process. Managing these biases requires continuous monitoring and careful training of the AI systems, alongside a critical review by human editors.

The integration of AI into the peer review process raises several ethical issues. First, transparency is needed. Authors and reviewers must understand when and how AI tools have been used.13 Additionally, accountability becomes a concern: if an AI tool provides misleading information or incorrect recommendations, it can be unclear who is responsible for these errors. Finally, there is the concern that AI could sometimes displace human judgment, reducing the role of peer reviewers.14 Ethical guidelines must be established to ensure that AI is used responsibly and does not undermine the integrity of the review process.

Moreover, handling sensitive and unpublished research data through AI systems brings potential risks to data privacy and intellectual property protection.1516 AI tools often process vast amounts of information, and without clear protocols, sensitive data could be at risk of leaks or misuse.1617 Robust security measures must be implemented to protect intellectual property, including encryption, secure data storage, and controlled access. Furthermore, users must understand how their data is used and what protections are in place to ensure its security throughout the review process.

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AI can significantly reduce the burden on reviewers by automatically flagging grammatical errors, spelling mistakes, and awkward phrasing.18 By correcting language issues, the AI allows reviewers to focus on the more critical scientific content, such as methodology, data interpretation, and overall coherence of the manuscript. This tool enhances the clarity and readability of academic papers, leading to more polished submissions and faster review processes. AI can complement traditional editing, improving the quality of the text before it even reaches the reviewers.

Ensuring consistency across the entire manuscript is critical for both accuracy and readability. AI can help detect inconsistencies in terminology, references, and data reporting.19 For instance, it can highlight if terms are used differently in various parts of the manuscript or if a reference mentioned in the text does not match the reference list. This saves reviewers time and helps authors adhere to journal-specific guidelines, ensuring that the manuscript is well-structured and coherent throughout.

AI is becoming an important tool for uncovering potential ethical issues, such as plagiarism or data manipulation.20 AI, with its ability to process large amounts of text and cross-reference databases, can compare submitted manuscripts to vast repositories of academic literature.21 This allows the AI to detect similarities with existing texts and raise concerns about plagiarism or the integrity of the presented data. Such automated detection helps reviewers focus on verifying scientific validity, as ethical concerns can be addressed early in the review process. This tool can complement existing plagiarism detection software, adding another layer of careful examination.

A key challenge for reviewers is structuring their feedback effectively. AI can assist by generating structured review templates that guide reviewers through key aspects of the manuscript.22 These templates can include sections on originality, methodology, ethical considerations, and overall contribution to the field. In addition, reviewers can develop a personalized review format by utilizing established recommendations23 already published and widely recognized. AI helps ensure that no key element is overlooked and that feedback is presented systematically and clearly. It can also draft initial comments based on manuscript content, helping reviewers save time.

Providing constructive, respectful, and clear feedback is essential for maintaining positive communication between reviewers and authors.24 AI can assist by drafting polite yet firm feedback that helps convey critiques without being dismissive or unclear. This is particularly important in peer review, where clear and constructive communication can foster better revisions and resubmissions. AI tools can help draft responses that minimize misunderstandings, reduce overly critical language, and maintain a professional tone, making the peer review process more collaborative and efficient.

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It is important to emphasize that AI programs should be used to enhance human expertise, not replace it.2526 Although AI performs exceptionally well in processing large datasets, managing repetitive tasks, and generating initial drafts or suggestions, it lacks the ability to understand the contextual, and ethical dimensions of academic content.27 The human touch remains crucial, especially in understanding the intent behind complex research and making value-based judgments about content quality. Editors and reviewers bring a depth of expertise,2829 intuition, and critical thinking that AI, despite its capabilities, cannot fully replicate. AI tools can accelerate certain aspects of peer review by helping with tasks like plagiarism checks, grammar corrections, or flagging common issues. However, the ultimate responsibility for decisions should always lie with experienced professionals who ensure the academic integrity of the work.

Clear training and guidelines for editors and reviewers are essential to ensure that AI tools are used effectively in academic settings.30 Editors and reviewers should be trained on when and how to integrate AI into their workflow, how to interpret the results provided by AI, and when human judgment should take priority. Clear and structured guidelines are essential to ensure consistent and appropriate use of AI tools, reducing the chances of misuse or over-reliance. Training programs should emphasize the practical application of these tools and an awareness of their inherent biases, promoting ethical practices and transparency throughout the review process.

JAMA and the JAMA Network journals have introduced guidelines to regulate using AI tools in academic publishing and peer review.31 The policy prohibits AI tools from being listed as authors and mandates transparency when AI is used in preparing manuscripts or research. Authors must disclose details about the AI tools used and take responsibility for the content generated by these technologies. Peer reviewers are also guided on the responsible use of AI, with clear rules regarding the confidentiality of submitted manuscripts.31 These measures align with broader efforts to ensure accountability and human oversight in scholarly publishing, including those by the International Committee of Medical Journal Editors (ICMJE)10 and the Committee on Publication Ethics (COPE).32 However, the JAMA guidelines on peer review AI use are somewhat limited. These guidelines only state that no part of a manuscript may be entered into an AI tool. If a reviewer has used AI, they must identify the tool and describe how it was used. Also, reviewers must assume full responsibility for all content of their review. To overcome the difficulties of enforcing ethical guidelines, academic journals might introduce specialized committees focused on monitoring the use of AI technologies, ensuring their application aligns with established ethical principles. Furthermore, organizing training programs for editors and reviewers could help them better understand AI’s potential and limitations, fostering more thoughtful and responsible usage.

This review aimed to systematically evaluate the advantages and disadvantages of AI use in peer review and publishing processes. Based on this assessment, we have developed a set of recommendations for reviewers designed to support an efficient and ethically sound review process (Table 1).

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Integrating AI in academic writing and editing processes necessitates a commitment to transparency, particularly in disclosing the specific prompts and parameters used to guide these models.27 Academic integrity demands identifying any AI-generated content, allowing readers and reviewers to assess its contribution and verify its relevance.33 Providing details about the prompts ensures also accountability and supports reproducibility in research workflows. Authors can foster trust and maintain ethical standards in scholarly communication by incorporating appendices or supplementary materials that outline the AI tools and prompts used.

Transparent reporting also addresses the challenges posed by the “black box” nature of many AI systems, which can mask the reasoning behind their outputs. Clear documentation and user-centric tools can demystify these processes, enabling reviewers and editors to evaluate AI contributions critically.34 This is especially important in academic publishing, where insightful interpretation and contextual understanding are often required. As AI cannot fully replicate these human qualities, transparency is a bridge, ensuring that the technology complements rather than undermines expert judgment.

Ultimately, the responsible use of AI in manuscript preparation hinges on its role as a supportive tool that enhances human expertise without displacing it.35 By adopting practices that highlight the use of AI tools and the associated methodological frameworks, the academic community can harness their efficiencies while upholding the principles of ethical scholarship.

Transparency in AI applications maintain trust in scholarly work and sets a standard for integrating emerging technologies responsibly and effectively. To ensure that large language models produce outputs that are academically robust, ethically sound, and aligned with scholarly integrity, users should adhere to the certain principles when designing prompts (Table 2).

Future studies could explore the development of AI tools specifically designed to assess the quality of scientific evidence within manuscripts, potentially using advanced natural language processing techniques. Additionally, comparative studies assessing the effectiveness of AI-driven versus traditional peer review processes in detecting errors and biases could provide valuable insights.

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The integration of AI into academic publishing brings notable advantages, such as streamlining repetitive tasks and improving the efficiency of peer review. Nonetheless, its shortcomings in specialized knowledge, contextual interpretation, and ethical decision-making emphasize the need for human involvement. Maintaining academic integrity requires transparency, effective bias management, and strong measures for data protection.

Rather than replacing human expertise, AI should serve as a supportive tool, guided by comprehensive policies and adequate training. By combining AI’s capabilities with ethical standards, the academic community can refine the peer review process while preserving its fundamental values.

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