Evaluation of Large Language Models for Peer Review in Transplantation Research: Algorithm Validation Study.

ABSTRACT:

Background

Peer review remains central to ensuring research quality, yet it is constrained by reviewer fatigue and human bias. The rapid rise in scientific publishing has worsened these challenges, prompting interest in whether large language models (LLMs) can support or improve the peer review process.

Objective

This study aimed to address critical gaps in the use of LLMs for peer review of papers in the field of organ transplantation by (1) comparing the performance of 5 recent open-source LLMs; (2) evaluating the impact of author affiliations-prestigious, less prestigious, and none-on LLM review outcomes; and (3) examining the influence of prompt engineering strategies, including zero-shot prompting, few-shot prompting, tree of thoughts (ToT) prompting, and retrieval-augm

SUBMITTER: Shen SM 

PROVIDER: S-EPMC12936655 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature