12,637 papers · updated 10 Oct 2026livingmeta.ai
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Research theme

AI Peer Review

The AI Peer Review theme comprises 266 papers in this corpus published between 1981 and 2026. Work here is dominated by Empirical Study, Experimental, Design Science. 5 open research gaps have been surfaced in this area.

Methodology profile

  • Empirical Study45 (17%)
  • Experimental33 (12%)
  • Design Science29 (11%)
  • Position Paper29 (11%)
  • Literature Review26 (10%)
  • Case Study22 (8%)

Research domains

  • Peer Review262 (98%)
  • Academic Writing2 (1%)
  • Human-AI Collaboration1 (0%)
  • Research Integrity1 (0%)

Frequent sub-topics

evaluative misalignment in LLM-assisted policy analysis and qualitative coding · 1LLM-based reviewer matching using deep retrieval and deep thinking with information-gain rewards · 1peer review challenges and AI integration in Latin American publishing · 1LLM clinical decision support in oncology multidisciplinary tumor boards · 1LLM-radiologist collaboration in medical reporting · 1ChatGPT evaluation of student essays compared to human assessment · 1RAG-based evidence assessment for peer review · 1bias in LLM-assisted peer review · 1

Open research gaps

  • The authors identify several gaps: (1) Limited application beyond computer science domain; (2) Resource constraints limiting exploration of larger model sizes; (3) Need for more sophisticated designs
  • The authors identify the need for enhanced guidance directed at authors, peer reviewers, and editors regarding the ethical and appropriate application of AI tools in scholarly publishing.
  • The paper identifies the need for improved detection methods specifically designed for peer-review contexts, and suggests that current AI detectors are insufficient for policy enforcement in academic
  • Future multimodal LLMs should improve chart understanding to better support scientific claim verification. The authors identify a critical gap in current models' ability to generalize across different
  • Extending SoundnessBench to other venues beyond ICLR and scientific domains such as biology, chemistry, and social science

Representative papers

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