<p>Despite changing times, peer review remains an integral part of academic publishing that upholds the credibility, integrity, and legitimacy of scientific research. Yet today, this very system is faltering under immense pressure. Surging submission volumes, increasing&nbsp;interdisciplinarity, and heightened scrutiny of ethical standards are pushing traditional peer review frameworks to their breaking point. Certain journals have already taken it upon themselves to innovate and move from traditional models of the past.&nbsp;<br><br>It is becoming painfully apparent that the persistent challenges including reviewer fatigue, bias, inefficiencies, and integrity lapses cannot be meaningfully addressed by isolated improvements. Incremental upgrades, however well-intentioned, are no longer enough. What's needed is a radical transformation–one that reshapes the peer review process from the ground up.</p><p>&nbsp;</p><h2>Deep Rooted Issues in Peer Review Systems</h2><p>The current system is strained by several core challenges. From assigning reviewers to coordinating revisions, editorial workflows are riddled with repetitive tasks. Editors and reviewers alike spend valuable time on administrative duties that shift their focus from scientific merit.<br>One major issue that often tops the headlines is reviewer fatigue and shortage. A&nbsp;<a href="https://link.springer.com/article/10.1007/s00359-023-01642-w?utm_campaign=related_content&amp;utm_source=HEALTH&amp;utm_medium=communities">recent empirical analysis</a> debunked this claim as a myth, revealing no significant change in average number of invitations per submission or response times. However, this could also be an isolated case for journals that rely on editors personally contacting potential reviewers to perform better in terms of reviewer engagement. It is important to recognise the dramatic increase in scholarly research output with an estimated&nbsp;<a href="https://ncses.nsf.gov/pubs/nsb202333">3.3 million articles published in 2022</a>–which puts immense pressure on scholarly publishing workflows.&nbsp;<br><br>Furthermore, traditional methods of peer review are increasingly failing to flag sophisticated forms of misconduct and conflicts of interest, leading to significant research integrity concerns. The rise of AI also contributes to these problems, facilitating issues like the intentional rephrasing of text to bypass AI checks ("<a href="https://arxiv.org/abs/2107.06751">tortured phrases</a>") and paper mills that produce fake research. Today’s reviewers must go far beyond traditional manuscript evaluation. They are now expected to safeguard research integrity by detecting image duplication, citation cartels, potential authorship for sale, image manipulation, and plagiarism, all while still providing rigorous, critical analysis of the scientific content.<br>Adding to these challenges are inconsistency and bias inherent in human-dependent processes. Subjectivity, unconscious biases, preferences, and prejudices can affect fairness and trust in the review process. While AI is designed to extract actionable intelligence for informed decisions, it is modelled on human data and thus can perpetuate and even cement existing biases, while disguising them as “objective”.</p><p>&nbsp;</p><h2>Why Upgrades Aren’t Enough&nbsp;</h2><p style="margin-left:0cm;">In an attempt to address these challenges, many publishers have employed various strategies such as incorporating open peer review, reviewer recognition platforms like Publons, and integrating AI tools for plagiarism detection. While these solutions aim to improve transparency and efficiency, they fall short of addressing the root problems.&nbsp;<br><br>Open peer review, for instance, does not streamline the process, it complicates it. Despite the good intentions of improving transparency and accountability, it&nbsp;<a href="https://pmc.ncbi.nlm.nih.gov/articles/PMC10653933/">introduces issues</a> about confidentiality, fear of potential discrimination and retaliation. Recognition programs promise to uplift reviewer morale, it does little to resolve fatigue or balance workloads. Not to mention the financial burden it creates by eating into editorial budgets, and the resultant scalability and sustainability issues that these monetary incentives come along with. This is not a sustainable path forward. Task-specific AI tools lacks the contextual understanding needed for complex ethical checks or accurate reviewer matching. These piecemeal upgrades may offer temporary relief, but they are just temporary fixes that fail to confront the systemic dysfunction undermining peer review. What is needed is a transformative approach—one that can address the complexity, scale, and ethical demands of modern peer review at their core. This is where agentic AI emerges as a game-changer, offering capabilities far beyond those of conventional AI tools.<br>&nbsp;</p><h2>Understanding Agentic AI and Its Potential</h2><p>While AI broadly refers to machines simulating human behavior, and machine learning (ML) refers to learning from data, agentic AI marks a new phase in which autonomous systems can independently perceive, reason, plan, and act with minimal human intervention.&nbsp;Unlike traditional, task-specific AI or generative AI focused primarily on content creation, agentic AI&nbsp;<a href="https://arxiv.org/html/2505.10468v1#S2">dynamically adapts to changing contexts</a> and continuously learns to optimize outcomes.&nbsp;<br>Core&nbsp;<a href="https://arxiv.org/pdf/2503.08979">technical capabilities</a> of agentic AI include:</p><ul style="list-style-type:disc;"><li>Probabilistic decision-making that involves using statistical models to weigh multiple factors and recommend the most effective editorial actions.</li><li>Natural Language Processing which enables AI to understand complex scientific text and interpret nuanced editorial instructions accurately.</li><li><a href="https://arxiv.org/html/2505.10468v1#S4">Multi-agent collaboration</a> that coordinates specialized AI agents to handle different editorial tasks simultaneously and efficiently.</li><li>Continuous learning which allows AI to improve over time by adapting to feedback, publication trends, and updated ethical standards.<br>With these capabilities, agentic AI systems are primed to tackle complex challenges. In the context of scholarly publishing, it offers the potential to address the core challenges plaguing peer review.</li></ul><h2>&nbsp;</h2><h2>Potential Agentic AI Use Cases Driving the Peer Review Reboot</h2><p>Agentic AI can facilitate the&nbsp;<a href="https://arxiv.org/html/2503.08979v1">transformation of peer review</a> by taking on many of the administrative and preliminary checks that currently burden human reviewers and editors. Here are a few major use cases where agentic AI shows promise:</p><ul style="list-style-type:disc;"><li><strong>Bias Detection and Ethical Oversight</strong><br>Agentic AI can flag discrepancies, suggest double-anonymized alternatives, and ensure compliance with COPE and other global guidelines by continuously monitoring submissions and reviews.</li><li><strong>Real-Time Editorial Support</strong><br>From advanced screening and integrity checks to assisting editors with meta-reviews, agentic AI accelerates decision-making without compromising quality.</li><li><strong>Reviewer Assistance</strong><br>Reviewers can use AI for summarizing complex manuscripts, identifying key references, and structuring feedback, saving time while enriching critique.</li></ul><ul><li><strong>Transparency and Ownership in Open Review</strong><br>Agentic AI can verify reviewer identity, detect ghost-written AI reviews, and ensure accountability, all while facilitating new models like public and post-publication peer review.</li><li><strong>Scalable Oversight for Publishers</strong><br>With adaptive AI systems, publishers gain consistent, scalable processes that uphold editorial standards even as submission volumes grow.<br>&nbsp;</li></ul><h2>The AI Reboot of Peer Review: What It Means for Publishers&nbsp;&nbsp;</h2><p>Embracing agentic AI in peer review is not optional but a strategic necessity that combines AI and human expertise to augment, rather than replace, editorial judgment. Publishers must build systems that evolve with research trends and integrate bias mitigation and transparency at every step. Clear documentation, user‑friendly tools, and disclosure of AI use, guided by standards like JAMA, ICMJE, and COPE, are essential to maintain public trust in the publishing process.&nbsp;<br><br>Peer review doesn’t just need an update, it needs a reboot that reimagines its very foundations. By embracing systemic transformation, the academic community can restore trust, efficiency, and integrity to scholarly publishing, ensuring that peer review continues to remain the cornerstone of credible science.&nbsp;<br><br>Now is the time for publishers, editors, and researchers to lead this transformation and not wait for the system to break under its own weight. The future of credible science depends on the choices we make today. Let’s commit to building a peer review system that is truly fit for the challenges of tomorrow.<br>&nbsp;</p><h2>References:</h2><ol><li><a href="https://www.ibm.com/think/insights/agentic-ai">https://www.ibm.com/think/insights/agentic-ai</a></li><li><a href="https://hbr.org/2024/12/what-is-agentic-ai-and-how-will-it-change-work">https://hbr.org/2024/12/what-is-agentic-ai-and-how-will-it-change-work</a></li><li><a href="https://aisera.com/blog/agentic-ai/">https://aisera.com/blog/agentic-ai/</a></li><li><a href="https://www.techtarget.com/searchenterpriseai/definition/agentic-AI">https://www.techtarget.com/searchenterpriseai/definition/agentic-AI</a></li><li><a href="https://scholarlykitchen.sspnet.org/2023/09/29/ending-human-dependent-peer-review/">https://scholarlykitchen.sspnet.org/2023/09/29/ending-human-dependent-peer-review/</a></li><li><a href="https://scholarlykitchen.sspnet.org/2024/09/24/guest-post-is-ai-the-answer-to-peer-review-problems-or-the-problem-itself/">https://scholarlykitchen.sspnet.org/2024/09/24/guest-post-is-ai-the-answer-to-peer-review-problems-or-the-problem-itself/</a></li><li><a href="https://scholarlykitchen.sspnet.org/2024/10/09/guest-post-overcoming-skepticism-through-experimentation-the-role-of-ai-in-transforming-peer-review/">https://scholarlykitchen.sspnet.org/2024/10/09/guest-post-overcoming-skepticism-through-experimentation-the-role-of-ai-in-transforming-peer-review/</a></li><li><a href="https://scholarlykitchen.sspnet.org/2023/09/29/ending-human-dependent-peer-review/">https://scholarlykitchen.sspnet.org/2023/09/29/ending-human-dependent-peer-review/</a></li><li><a href="https://scholarlykitchen.sspnet.org/2024/09/20/peer-review-week-2024-ask-the-chefs/">https://scholarlykitchen.sspnet.org/2024/09/20/peer-review-week-2024-ask-the-chefs/</a></li><li><a href="https://jkms.org/pdf/10.3346/jkms.2025.40.e92">https://jkms.org/pdf/10.3346/jkms.2025.40.e92</a></li><li><a href="https://www.kwglobal.com/blog/the-year-in-ai/">https://www.kwglobal.com/blog/the-year-in-ai/</a></li><li><a href="https://publishingstate.com/ai-in-peer-review/2024/#google_vignette">https://publishingstate.com/ai-in-peer-review/2024/#google_vignette</a></li><li><a href="https://www.csescienceeditor.org/article/human-attention-observation-editorial-peer-review/">https://www.csescienceeditor.org/article/human-attention-observation-editorial-peer-review/</a></li><li><a href="https://www.highwirepress.com/blog/ai-and-human-interactions-in-scholarly-publishing/">https://www.highwirepress.com/blog/ai-and-human-interactions-in-scholarly-publishing/</a></li></ol><p style="margin-left:54.0pt;">&nbsp;</p>

Why Peer Review Needs a 'Reboot' (Not Just an Upgrade)

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