AI for scientific integrity: detecting ethical breaches, errors, and misconduct in manuscripts.

ABSTRACT: The use of Generative AI (GenAI) in scientific writing has grown rapidly, offering tools for manuscript drafting, literature summarization, and data analysis. However, these benefits are accompanied by risks, including undisclosed AI authorship, manipulated content, and the emergence of papermills. This perspective examines two key strategies for maintaining research integrity in the GenAI era: (1) detecting unethical or inappropriate use of GenAI in scientific manuscripts and (2) using AI tools to identify mistakes in scientific literature, such as statistical errors, image manipulation, and incorrect citations. We reviewed the capabilities and limitations of existing AI detectors designed to differentiate human-written (HWT) from machine-generated text (MGT), highlighting performance gap

SUBMITTER: Pellegrina D 

PROVIDER: S-EPMC12436494 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature