AI-Powered Manuscript Screening: How Artificial Intelligence Is Transforming Journal Submission Evaluation

Introduction

The rise of artificial intelligence (AI) is reshaping almost every stage of the scholarly publishing workflow, and one of the most visible areas of change is manuscript screening. Journals and conferences now receive unprecedented numbers of submissions from all over the world. Editorial teams must quickly decide which manuscripts are suitable for peer review, which require revision before even being considered, and which fall outside the journal’s scope altogether.

Traditionally, this initial triage has relied on manual checks: editors and editorial assistants verify formatting, reference style, word counts, ethics statements, and basic relevance. They also screen for plagiarism and obvious data or image manipulation. This is time-consuming, repetitive work that delays peer review and can strain the capacity of editorial offices. It is also susceptible to human error and unconscious bias.

AI-powered manuscript screening tools aim to address these challenges by automating repetitive, rules-based tasks and providing data-driven support for editorial decisions. By combining natural language processing (NLP), machine learning (ML), and automated analysis of text, images, and metadata, AI systems can help ensure that only compliant, relevant, and ethically sound manuscripts move forward to peer review. This article examines how AI is used in manuscript screening, the benefits and risks involved, and how publishers can integrate these tools responsibly.

The Limits of Traditional Manuscript Screening

Before discussing AI solutions, it is important to understand why manual screening is under such strain.

1. Rising Submission Volumes

Open-access publishing, global research growth, and increasing publication pressure have driven submission numbers to record levels. Many journals receive thousands of manuscripts per year. Even a simple initial check—confirming word count, section structure, and basic suitability—can quickly create backlogs.

2. Labour-Intensive Preliminary Checks

Editors and editorial assistants must verify that each manuscript:

• follows the journal’s formatting and reference style;
• includes required sections (e.g., abstract, methods, ethics, funding statements);
• meets word and figure limits;
• contains appropriate disclosures (e.g., conflicts of interest, trial registration);
• complies with basic ethical and reporting guidelines.

When carried out manually, this work is repetitive and slow, diverting time away from higher-level editorial tasks such as conceptual evaluation and reviewer management.

3. Plagiarism, Image Manipulation, and Data Integrity

Research integrity issues—such as plagiarism, self-plagiarism, duplicate submissions, fabricated data, and manipulated figures—are a growing concern. Detecting these problems requires comparing submissions against large bodies of published literature and image archives. Human editors cannot do this efficiently without automated help.

4. Reviewer Overload and Misrouted Papers

Many manuscripts are sent to journals where they do not truly belong. Misalignment between the paper’s topic or methods and the journal’s scope leads to avoidable desk rejections or, worse, wasted reviewer time. Poorly structured or clearly unsuitable manuscripts sometimes slip into peer review simply because editorial teams are overwhelmed.

5. Bias and Inconsistency

Human editors inevitably bring their own experiences and preferences to the process. Without clear, standardised criteria, initial screening can vary between individuals, and implicit biases related to country, institution, or topic can subtly influence decisions.

How AI Transforms Manuscript Screening

AI-based tools are designed to complement, not replace, human editors. They take over the mechanical, rules-based parts of screening and provide signals that help editors decide which manuscripts deserve closer attention.

1. Automated Formatting and Compliance Checks

One of the most straightforward uses of AI is to automatically verify whether a submission meets a journal’s technical requirements. AI-driven systems can:

• check reference and citation style against journal preferences (APA, MLA, Chicago, Vancouver, etc.);
• confirm that the manuscript is within word, figure, and table limits;
• inspect the structure of sections (e.g., presence of Abstract, Introduction, Methods, Results, Discussion, Conclusion);
• detect missing elements such as ethics approvals, consent statements, or conflict-of-interest disclosures.

Tools like Penelope.ai and similar systems run these checks almost instantly at submission, generating a report for authors and editors. Authors can then correct basic issues before the editor even looks at the manuscript.

2. AI-Based Plagiarism and Image Manipulation Detection

Plagiarism detection has long relied on automated text comparison, but AI-enhanced tools take this further by recognising paraphrased passages, self-plagiarism, and subtle forms of duplication. Systems such as iThenticate compare submissions against extensive databases of articles, books, and web content to flag suspicious overlaps.

For figures and images, dedicated tools like Proofig analyse images for signs of duplication, inappropriate reuse, or manipulation. They can highlight repeated panels, cloned regions, or surprising transformations that may indicate deliberate misconduct or careless figure preparation.

These tools do not make final judgements—they raise flags for editors to review carefully. Used properly, they strengthen research integrity and protect journals from publishing problematic work.

3. Language and Readability Support

Many submissions are scientifically sound but difficult to read due to language issues, particularly when authors are writing in a second or third language. AI language tools can help improve:

• grammar, spelling, and punctuation;
• sentence structure and overall readability;
• clarity of argument and academic tone;
• terminology consistency across the manuscript.

Services such as Trinka AI and similar editors are tailored to academic writing and can be used by authors before submission or by journals as part of pre-screening. While language quality should not be used as a proxy for scientific merit, improving clarity makes it easier for editors and reviewers to evaluate the actual research.

4. Relevance and Scope Matching

Another valuable use of AI is to determine whether a submission fits a journal’s aims and scope. By analysing keywords, abstracts, and subject classifications, AI models can:

• assign manuscripts to topic categories or subfields;
• flag submissions that are clearly outside the journal’s remit;
• suggest appropriate associate editors or subject editors;
• help identify suitable peer reviewers by matching manuscript topics with researcher expertise and publication history.

Tools like Clarivate’s Reviewer Finder and other AI-driven recommender systems use citation data and keyword analysis to support this matching process. This can reduce reviewer overload and ensure that manuscripts are evaluated by experts in the right niche.

5. Novelty and Statistical Integrity Checks

More advanced AI tools are beginning to assess aspects of novelty and methodological soundness. By comparing a submission to large bodies of existing literature, AI can indicate whether similar work has recently been published, or whether the manuscript appears to duplicate prior studies without clear justification.

In experimental and clinical research, systems such as StatReviewer can automatically check:

• whether statistical tests match the study design and data type;
• whether effect sizes, confidence intervals, and p-values are reported correctly;
• whether sample sizes and power calculations are adequate and transparently documented.

Again, these tools do not replace expert statisticians, but they can highlight potential issues early, allowing editors to request clarification or additional review.

Challenges and Ethical Questions

While AI offers impressive benefits, it also introduces new challenges that must be handled carefully.

1. Over-Reliance on Automation

If editors lean too heavily on automated scores or flags, they may unintentionally reject valid research that does not fit expected patterns or that uses unconventional methods. Complex, interdisciplinary, or innovative submissions can confuse algorithms trained on more standard formats.

The solution is to treat AI outputs as advisory, not decisive. AI should help prioritise attention, not replace editorial judgement.

2. Algorithmic Bias

AI systems learn from the data on which they are trained. If those data reflect historical biases—for example, favouring certain topics, methods, languages, institutions, or regions—the AI may inadvertently reinforce those patterns. This risks amplifying inequities that many publishers are actively trying to reduce.

Responsible use of AI requires:

• regular auditing of models for biased outcomes;
• transparent documentation of how models are built and updated;
• ongoing human oversight to question and correct problematic patterns.

3. Data Privacy and Security

Manuscripts under review are confidential and often contain unpublished data, proprietary methods, or sensitive information. Any AI system that processes submissions must therefore comply with strict data protection standards. Publishers must ensure that:

• uploaded manuscripts are stored securely and not used for unrelated training without explicit permission;
• access to AI platforms is controlled and monitored;
• third-party vendors comply with privacy regulations and contractual obligations.

Best Practices for Responsible Integration of AI

To harness the benefits of AI while avoiding its pitfalls, journals and publishers can adopt several best practices:

• Define clear roles for AI and humans: Use AI for preliminary checks and support, but keep final decisions in the hands of experienced editors.
• Be transparent with authors and reviewers: Explain which AI tools are used, for what purposes, and how their outputs influence editorial workflows.
• Monitor performance and fairness: Regularly review how AI-assisted screening affects turnaround times, acceptance rates, and diversity of published authors and topics.
• Provide training for editorial staff: Editors should understand the strengths and limitations of the tools they are using, so they can interpret outputs critically.
• Maintain multiple safeguards: Combine AI checks with plagiarism tools, human integrity review, and clear policies on handling flagged manuscripts.

Conclusion

AI-powered manuscript screening has the potential to transform the submission evaluation process. By automating compliance checks, plagiarism detection, image analysis, language refinement, relevance matching, and basic statistical review, AI tools can significantly reduce editorial workload, shorten decision times, and enhance the integrity of the published record.

However, AI is not a replacement for the nuanced judgement and ethical responsibility of human editors, reviewers, and publishers. The most robust systems will be hybrid workflows in which AI handles repetitive technical tasks while humans retain authority over scientific merit, fairness, and final decisions. Used thoughtfully, AI can help academic publishing become faster, more consistent, and more transparent—without sacrificing the rigour and trust on which scholarly communication depends.