AI-Driven Manuscript Screening: Benefits, Limitations & Best Practices

12 Jan 2026

The rapid growth of scholarly publishing has led to a sharp increase in manuscript submissions across journals. Editors today must handle higher volumes while maintaining quality, ethical standards, and timely decisions. To address these challenges, many publishers are adopting AI-driven manuscript screening as part of their editorial workflow.

AI-powered screening tools are designed to assist editors by automating early-stage checks, reducing manual workload, and improving efficiency. However, while AI brings clear advantages, it also introduces important limitations that publishers must carefully evaluate.

This blog explores how AI-driven manuscript screening works, its key benefits, and its limitations in modern scholarly publishing.

AI-driven manuscript screening uses artificial intelligence and machine learning algorithms to evaluate manuscripts at the initial stages of submission.

These systems typically analyze:

• Manuscript structure and completeness

• Language quality and readability

• Plagiarism or content similarity

• Scope and topic relevance

• Basic ethical and formatting compliance

The goal is not to replace editors, but to support editorial teams by identifying potential issues early in the workflow.

AI screening tools process manuscripts using:

• Natural Language Processing (NLP) to understand text structure and meaning

• Pattern recognition to identify similarities or anomalies

• Rule-based logic aligned with journal guidelines

• Historical data from previous editorial decisions

Based on this analysis, the system flags manuscripts that may require further attention before peer review.

AI enables rapid screening of submissions, allowing editors to identify unsuitable manuscripts within minutes rather than days. This significantly reduces editorial backlog.

By automating repetitive checks, AI frees editors to focus on higher-value tasks such as reviewer selection, decision-making, and content strategy.

AI applies screening criteria uniformly across submissions, minimizing inconsistencies caused by human subjectivity or workload pressure.

AI can identify language problems, formatting gaps, and structural inconsistencies before peer review, improving overall submission quality.

Early screening allows authors to receive quicker feedback, improving transparency and satisfaction in the submission process.

AI systems struggle to fully understand research novelty, theoretical contribution, or nuanced arguments—areas that require expert human judgment.

AI models trained on historical data may unintentionally reinforce existing biases related to language, region, or research approach.

Excessive dependence on AI may lead to premature rejection of manuscripts that require careful human evaluation.

AI screening tools may perform well in structured scientific disciplines but face limitations in humanities or interdisciplinary research.

Authors may question how screening decisions are made if AI processes are not transparent or well-documented.

The most effective use of AI-driven manuscript screening is as a decision-support tool, not a decision-maker.

Best practices include:

• Combining AI screening with human editorial review

• Using AI for initial checks, not final decisions

• Allowing editors to override AI flags

• Clearly communicating AI usage to authors

This hybrid approach ensures efficiency without compromising editorial integrity.

As AI models evolve, future screening systems may:

• Better understand research context and methodology

• Adapt to journal-specific policies

• Provide explainable insights rather than binary flags

• Integrate seamlessly into end-to-end JMS platforms

However, human oversight will remain essential to ensure fairness, quality, and ethical publishing.