Scientific article review platform using generative artificial intelligence to streamline the peer review process

Abstract

This study introduces a novel Generative Artificial Intelligence (GAI) platform designed to streamline the peer review process. By analyzing a case study of 10 scientific articles, we demonstrate that GAI effectively evaluates article quality and pinpoints specific areas requiring improvement. Our platform achieves an average similarity of 63.6% with human reviewers, enabling the automation of routine evaluation tasks while enhancing both efficiency and objectivity. By drawing on recent generative AI benchmarks across research support, educational assessments, reviewer matching, and large-scale application studies, we demonstrate a focused, practically validated solution that not only aligns with but slightly outperforms general GAI performance levels, offering a transformative approach to real-world manuscript evaluation.

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