AI-assisted integrity checks are entering peer review because journals need to identify concerns while there is still time to act. The aim is not to let AI judge a manuscript. It should not. The aim is to place screening at useful points in the workflow, so editors and research integrity teams can review signals before decisions become harder to change.
Integrity concerns are most difficult when they appear late. A concern raised after acceptance, during production, or after publication can force a journal to pause decisions, reopen correspondence, or hold files. Editors may have completed assessment, reviewers may have submitted reports, and authors may be expecting publication. Even a limited concern can then take time to resolve.
Earlier checks reduce that pressure. Some screening may belong at submission. Some may be useful after revision, when figures, declarations, or text may have changed. A final check before acceptance can help ensure unresolved concerns are not passed into production. The right placement depends on the journal, discipline, article type, and known risk areas.
AI can help by drawing attention to material that needs human review. Signals may include possible image alteration, duplicated panels, reused visual material, text similarity, undisclosed machine-generated material, unusual citation patterns, or repeated features across submissions. None of these signals is a conclusion. Each is a reason to examine the material.
Image screening shows why this work cannot be left only to conventional peer review. In many fields, figures carry much of the evidence, but reviewers are usually assessing the study, methods, interpretation, and contribution. They may not have the time, tools, or wider view needed to identify repeated image elements or reuse. Consistent image checking needs a defined place in the process.
The report reaching the editorial office matters as much as the screening. A vague alert or unexplained score is not useful. Staff need to see what was flagged, where it appears, why it may matter, and what route is suggested. The next step may be author clarification, replacement files, editor review, or integrity escalation.
Workflows also need clear ownership. Journals should know who reviews first-level flags, who contacts authors, who can close a concern, and what is recorded in the manuscript history. False positives must be expected. Similar wording, permitted reuse, preprint overlap, corrected files, or standard formats may explain a flag.
Different journals need different checks. Image-heavy submissions, review articles, data-intensive papers, and fast-moving collections carry different risks. Portfolio visibility matters too, because repeated signals may not be visible to one handling editor.
AI integrity checks are useful when they support editorial operations: early enough to matter, clear enough to act on, and governed enough to protect judgment. Their value is not in settling integrity questions, but in helping publishers notice concerns earlier and handle them with a stronger record. Know more
Knowledgespeak Editorial Team
