If the proverbial saying that Artificial Intelligence (AI) touches every aspect in life is true, the world of scientific publishing certainly is no exception. Its ecosystem faces two major challenges: keeping pace with the growing volume of research and preserving the trust in published findings. When integrated properly, AI has the potential of tackling both issues by streamlining workflows and introducing sophisticated detection systems that ensure accuracy and integrity. While AI itself can be used for fraudulent purposes, its power in detecting the same is undeniable. That’s why the human hand should never be far from its use.
Reshaping the landscape of scientific publishing, AI is transforming how research is written, reviewed, disseminated, accessed and communicated. Publishers, editors, and researchers are now leveraging machine learning, natural language processing and generative AI to improve quality, accelerate publication, and protect against misconduct.
Revolutionising manuscript preparation
For researchers, preparing a manuscript for publication has traditionally been time-intensive. Authors spend countless hours formatting citations, refining language, and ensuring compliance with journal guidelines. AI-powered tools such as Writefull and Trinka AI are automating these tasks, helping researchers produce publication-ready drafts faster and more accurately. These platforms improve grammar, check terminology, and align phrasing with academic writing standards. Non-native English speakers, in particular, benefit from language enhancement tools that make their research more competitive on a global scale.
Generative AI can further support authors by summarising findings, suggesting related studies, and proposing structural improvements. While human researchers remain responsible for scientific content, these tools free up time no longer spent on tedious, redundant tasks, such as formatting and stylistic details, in order to focus on the research itself. However, unless as a formal part of a study design, AI should never be used to generate research findings due to the risk of error, the technology’s tendency towards certain biases and the violation of scientific norms. That is why the peer review process remains a cornerstone in the scientific publishing process.
Enhancing peer review and research discoverability
Peer review is the foundation of scientific credibility, but traditional methods can often be slow and inconsistent. AI can enhance this process by assisting reviewers with tasks such as identifying missing or inappropriate citations, detecting methodological gaps, uncovering plagiarism and classifying references. Award-winning tools such as Scite.ai analyse citations to determine whether they support, dispute, or simply mention a reference, helping reviewers quickly assess a referenced study’s validity.
AI also improves research discoverability in an era where millions of studies are published annually. Platforms such as Semantic Scholar and Dimensions AI use semantic search
capabilities to understand context rather than relying on simple keyword matching. These tools map citation networks and highlight emerging trends, enabling researchers to find relevant studies more efficiently and conduct more comprehensive literature reviews.
Beyond discovery, AI-powered summarisation tools such as Scholarcy and Elicit make research easier to digest for scientists and policymakers alike. By generating concise summaries and extracting structured findings, these tools reduce the time needed to interpret complex research.
Making science more accessible for authors and patients alike
One of AI’s most significant contributions to publishing is improving accessibility. Tools such as DeepL Write offer high-accuracy translations that preserve technical nuance, helping journals reach global audiences. Lay summaries, text-to-speech services, and AI-powered metadata tagging also make research more approachable for non-specialists, early-career scientists, and readers from diverse linguistic backgrounds. Ludenso provides an award-winning solution for visually impaired readers. With its conversational AI interface based on printed text, readers can ask the very engaging tool a question and the AI provides an answer based on the content of the text. It also guides the reader with a page reference so they can read more about the specific topic they asked about.
By removing language and technical barriers, AI is helping democratise science, ensuring that breakthroughs are available to researchers and practitioners worldwide. In addition, AI tools such as HeyGen, a video remapping service, can translate videos into over 170 languages. It redubs using a real person based on a single image. Alternatively, the service provides high-quality cartoon characters, which make the information particularly accessible for patients.
Strengthening research integrity through AI-powered fraud detection
As AI accelerates publication, it is equally crucial in protecting the credibility of scientific literature. With all the opportunities afforded to researchers outlined above, there remains an opportunity for the misuse of technologies. Misconduct, including plagiarism, image manipulation, fabricated data, and paper mill submissions, has become a growing problem and is thought to be exacerbated by the growing availability of AI powered text and image generating tools. According to one analysis conducted by the Committee on Publication Ethics (COPE) and the STM Association, the study found that publishers reported that up to 46% of the submissions to a journal can be suspected of having some kind of paper mill involvement. Further, a study of researchers in the Netherlands on Questionable Research Practices estimated a self-reported prevalence of fabrication or falsification of 8.3%. The same study found that life and medical sciences had the highest self-reported prevalence of falsification or fabrication of all the disciplines surveyed. Therefore, there is a growing need to meet any rise in the rate of research or publication misconduct with an expansion in robust misconduct detection solutions. As a result of technological advances, publishers now have highly developed AI systems to detect fraud much earlier in the editorial process.
Plagiarism detection has evolved beyond simple word matching. Platforms such as Crossref Similarity Check and Copyleaks compare manuscripts against vast databases containing millions of published papers and billions of web pages. These tools can recognise direct text
reuse, help identify potential redundant publication, and indicate where relevant citations have been omitted before publication.
Inappropriate Image manipulation has emerged as another critical concern. In fields such as biology and medicine, falsified images can undermine entire studies. Another award-winning AI-powered tool, ImageTwin, analyses figures for duplication, splicing, and AI generated fabrications. Many leading journals have adopted these solutions to verify the authenticity of experimental visuals.
AI also helps to identify statistical anomalies. Tools such as reviewerzero review reported p-values and other statistical outputs to flag inconsistencies that might indicate reporting errors or potentially fabricated data. This level of automated validation strengthens reproducibility and protects the scientific record.
One of the biggest threats to publishing integrity comes from paper mills, which are organisations that mass-produce fraudulent research papers for profit, often including fabricated or falsified data, irrelevant citations and nonsense terminology known as ‘tortured phrases’. Publishers are increasingly adopting AI models designed to analyse writing styles, submission data, and unusual submission patterns to detect suspicious activity. ClearSkies, for instance, provides the Paper Mill Alarm for this purpose. As generative AI becomes more prevalent in the research ecosystem, the scholarly publishing industry are increasingly collaborating, for example through the STM Integrity Hub and United2Act initiative to utilise and develop new detection models capable of distinguishing inaccurate, misrepresentative or fabricated manuscripts from authentic research. At the centre of these efforts remains the need for human verification and review from journal teams, reviewers, editors and the research community. These safeguards ensure that automation enhances science rather than undermining its credibility.
How AI tools are driving change
Trusted AI-driven tools are shaping the way scientific publishing is conducted today. When used responsibly AI enables more innovation, accuracy, efficiency, and collaboration.
The future of AI in scientific publishing
AI’s role in scientific publishing is set to expand even further in the coming years. Integrated AI pipelines will soon connect manuscript preparation, peer review, quality control, and fraud detection in a seamless process. Publishers are exploring real-time fact-checking systems, automated metadata generation, and cross-journal collaboration to identify fraudulent activity faster and more effectively.
While there is a wide range of tools available to researchers and publishers today, the act of formally implementing them into their workflow is still in its early stages. Transparent policies around AI-assisted writing, disclosure requirements, and editorial oversight will be critical to maintaining trust. Publishers must balance innovation with responsibility, ensuring that automation strengthens, not compromises, the quality of scientific research. The future of publishing will depend on the thoughtful integration of AI with human expertise. When applied responsibly, AI has the potential to democratise access to knowledge, accelerate innovation, and uphold the highest standards of research integrity, thereby ensuring that science continues to advance for the benefit of society.
