Artificial intelligence (AI) is becoming a common presence in scientific publishing, yet its use in peer-review has received much less attention than its role in manuscript preparation. This article aims to analyze the use the structural pressures that drive reviewers toward AI use, including time constraints, reviewer scarcity, and performance-based incentives. It contrasts critical human reading with automated or template-based reports, identifies recurrent signals of AI-assisted reviews, and examines their ethical, emotional, and sustainability implications. Attention is given to how AI may influence metric-based evaluations and reinforce superficial or score-driven interpretations of scientific quality. We argue that the central risk is not simply factual error, but the gradual normalization of procedural evaluation over intellectual scrutiny. As automation becomes routine, peer-review may shift from a space of critical dialogue to a system of opaque filters. To address this challenge, we propose the need for “meta-assessment” frameworks capable of evaluating not only scientific methods, but also the quality and transparency of the evaluation process itself. Moving forward, peer-review must integrate AI with human oversight and transparent standards, so that efficiency supports rather than replaces critical evaluation, contributing to greener and more sustainable practices in our community.
Fuente-Ballesteros et al. (Sat,) studied this question.
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