Kuladinithi, KoojanaKoojanaKuladinithiFuger, KonradKonradFugerMakama, AliyuAliyuMakamaTimm-Giel, AndreasAndreasTimm-GielKiener, MaximilianMaximilianKienerBozenhard, JonasJonasBozenhard2026-07-232026-07-232026-0324th IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, IEEE PerCom Workshops 2026https://hdl.handle.net/11420/64020The current scientific peer review process faces increasing strain and is becoming difficult to sustain. While large language models (LLMs) can help reviewers by summarizing papers, assessing clarity, and generating timely feedback, the research community remains cautious about relying on LLM-based reviews. In this paper, we explore how LLMs could support and potentially improve parts of the peer review process. We examine both the benefits and limitations by analysing LLM-generated reviews of our own previously published papers, comparing them with human reviewer feedback, identifying points of agreement and disagreement, and considering requirements for the responsible and ethical use of LLMs in peer review.enAIChatGPTEthicsLLMPeer-reviewComputer Science, Information and General Works::006: Special computer methods::006.3: Artificial Intelligence::006.31: Machine LearningComputer Science, Information and General Works::006: Special computer methods::006.3: Artificial IntelligencePhilosophy and Psychology::170: Ethics (Moral Philosophy)Conference Paper10.1109/PerComWorkshops68308.2026.11585353