MDPI rolls out AI integrity system to screen manuscripts submissions
MDPI has announced the full deployment of an in-house AI-powered research integrity system, Ethicality, which is now being used to automatically screen all manuscripts submitted to the publisher.
The company says the rollout marks a significant expansion of its research integrity infrastructure, with the system now screening approximately 2,000 submissions from authors worldwide each day.
MDPI says Ethicality functions as an integrated, end-to-end integrity layer embedded within the editorial workflow, continuously monitoring manuscripts rather than operating as a one-off screening tool.
Dr Milos Cuculovic, head of technology innovation at MDPI, said: “What is becoming clear is that traditional, manual processes are no longer sufficient in peer review. The industry needs to shift from reactive approaches, resolving issues after publication, to proactive systems that support editors earlier in the workflow. AI, when used responsibly, acts as a set of guardrails rather than a substitute for human judgment. The future lies in combining automation with strong editorial oversight to ensure consistency, transparency, and trust at scale.
“The publishing industry is undergoing a fundamental shift driven by scale and technology. Submission volumes continue to grow, while expectations around speed, transparency, and quality are increasing. At the same time, generative AI is creating both opportunities and risks – from improved workflows to challenges such as synthetic content, manipulated data, and questionable authorship practices.”
Ethicality analyses submissions by examining components including the title, abstract, author metadata, manuscript text and references before carrying out a broader integrity assessment. Peer review reports are also analysed as part of the process.
The system is designed to identify a range of potential integrity concerns, including paper mill activity, fabricated submissions, AI-generated or manipulated text, citation manipulation, irregular referencing patterns, fake references, author identity concerns, authorship anomalies, suspicious peer review activity and AI-generated text within peer review reports.
MDPI noted that it continues to use third-party technologies alongside Ethicality, including Proofig for image integrity checks and iThenticate for plagiarism detection and text duplication screening. It also emphasised that Ethicality is intended to support, rather than replace, editorial decision-making. While the platform provides automated analysis and risk indicators, all flagged cases are reviewed by human editors or research integrity specialists before any action is taken.
Dr Enric Sayas, product owner of Ethicality, said: “We are in a technological race. As generative AI makes it easier to produce sophisticated plagiarism and high-quality fake papers, traditional detection methods are no longer sufficient. The only viable response is to deploy equally advanced tools – using large language models and specialiaed AI systems to detect manipulated images, inconsistent data, and AI-generated content. Without such safeguards, the volume of fraudulent submissions risks overwhelming peer review and undermining the credibility of scholarly publishing.
“The primary value of AI is its ability to handle time-consuming aspects of manuscript processing, allowing editors and reviewers to focus on high-level scientific evaluation. Tasks such as reference validation, formatting checks, and basic technical triage are essential but repetitive. AI can perform an initial screening, flagging problematic cases for editorial review, ensuring that human expertise is applied where it matters most – scientific decision-making.”