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Large language models for peer review in biotechnology.

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Summary

Large Language Models (LLMs) offer substantive feedback for scientific manuscripts but are more lenient than human peer reviewers. AI can be a valuable ad hoc reviewer, though privacy and AI detection remain challenges.

Keywords:
Artifical General IntelligenceGPTGeminiPeer ReviewQwen

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Area of Science:

  • Biotechnology
  • Artificial Intelligence in Scientific Research

Background:

  • Researchers increasingly use Large Language Models (LLMs) for rapid manuscript feedback.
  • The reliability of AI as a peer reviewer in biotechnology is a critical question.

Purpose of the Study:

  • To evaluate the performance of AI reviewers (GPT-5, Qwen-Plus, Gemini 2.5 Pro) compared to human reviewers.
  • To assess the effectiveness of AI detectors in identifying AI-generated peer review comments.

Main Methods:

  • Analysis of 763 preprints and 12 grant proposals subjected to AI peer review.
  • Comparison of AI reviewer comments on experimental design, statistical analysis, and overall leniency against human reviews.
  • Testing AI detectors on AI-generated review comments.

Main Results:

  • AI reviewers provide substantive, well-structured comments, focusing on experimental design and statistics.
  • AI reviewers are generally more lenient than humans, rating grant proposals more favorably and requesting fewer citations.
  • AI detectors struggle to reliably identify AI-generated text in review comments due to LLM evolution.

Conclusions:

  • AI can function as a valuable, less biased ad hoc reviewer, but privacy and copyright concerns exist.
  • Developing AI detection tools and ensuring human oversight are crucial for integrating AI into peer review.
  • Future AI systems may rival or exceed human capabilities in scientific manuscript evaluation.