A survey of researchers who compared AI-generated scientific reviews with journal-agnostic human peer review reveals that they overwhelmingly prefer using AI as a self-checking tool before submission rather than as a replacement for human reviewers. It encourages an “author-centric” model in which AI helps researchers improve their manuscripts before they are reviewed by their peers.
AI’s Growing Role as Scientific Peer Reviewer | Stanford HAI
Stanford computer scientist James Zou is exploring how AI can accelerate scientific research and peer review. His finding: AI excels at spotting gaps, but judgment calls still need humans.
AI reviewers are here — we are not ready
Nature - Artificial intelligence promises rapid and polite feedback on papers — but we must first review the reviewer.
AI, peer review and the human activity of science
When researchers cede their scientific judgement to machines, we lose something important.
Major AI conference flooded with peer reviews written fully by AI
Nature - Controversy has erupted after 21% of manuscript reviews for an international AI conference were found to be generated by artificial intelligence.
AI-assisted Reviewing is Necessary and Should be Open
Peer review is facing a death spiral. AI production tools are speeding it up. AI-assisted reviewing is necessary and should be open.
AI-assisted Reviewing is Necessary and Should be Open
Peer review is facing a death spiral. AI production tools are speeding it up. AI-assisted reviewing is necessary and should be open.
Project Rachel: Can an AI Become a Scholarly Author?
This paper documents Project Rachel, an action research study that created and tracked a complete AI academic identity named Rachel So. Through careful publication of AI-generated research papers, we investigate how the scholarly ecosystem responds to AI authorship. Rachel So published 10+ papers between March and October 2025, was cited, and received a peer review invitation. We discuss the implications of AI authorship on publishers, researchers, and the scientific system at large. This work contributes empirical action research data to the necessary debate about the future of scholarly communication with super human, hyper capable AI systems.
AI-powered Literature Review & Synthesis
Recording from my workshop last week on smarter literature review with AI
Accelerating Science with Human+AI Review
This issue of NEJM AI features the first two articles published through our accelerated human+AI review process. In this editorial, we describe the invitation-only “Fast Track” process used to revi...
AI assistants can sway writers’ attitudes, even when they’re watching for bias | Cornell Chronicle
Cornell Tech researchers found that writers who used biased AI auto-suggestions saw their views gravitate toward the AI’s positions without their realizing it — even when they were made aware of the biased AI.
Towards Automating Scientific Review with Google's Paper Assistant Tool
Artificial intelligence is driving a revolution in scientific discovery, accelerating everything from hypothesis generation to mathematical theorem proving. However, this rapid acceleration is creating a systemic challenge: traditional human peer review cannot scale to match the influx of AI-assisted science. Ultimately, to resolve this tension, we must also deploy AI to accelerate the verification and review process itself. To frame the discussion around this transition, we propose a taxonomy consisting of four progressive levels of AI-human collaboration in scientific evaluation, and discuss various trade-offs involved with each. As a step toward this future, we introduce the Paper Assistant Tool (PAT), an agentic AI framework built for deep scientific review and verification. PAT ingests full scientific manuscripts and produces a comprehensive evaluation, checking theoretical results, validating experiments, suggesting improvements, and identifying potential flaws. By utilizing inference scaling techniques, PAT is able to identify deeper issues than a single model call alone, achieving a 34% improvement over zero-shot recall on mathematical errors in the SPOT benchmark. Pilot deployments of PAT as a pre-submission tool for authors at two major Computer Science conferences -- STOC and ICML -- demonstrate its ability to identify critical errors and suggest substantive improvements to research papers. By catching errors early, PAT eases the cognitive burden placed on referees, while preserving their control over the outcomes of the review process.
Absolutely incredible figure from this journal article arguing that AI should be accepted into the publication and peer review process. #MedSky doi.org/10.1515/cclm-2025-1180
Peer review is facing a death spiral, and AI production tools are speeding it up. AI-assisted reviewing is necessary and should be open. We built OpenAIReview: open AI reviewing for everyone, for the cost of a coffee. openaireview.github.io/blog.html 🧵
AI-assisted Reviewing is Necessary and Should be Open
openaireview.github.ioAbsolutely incredible figure from this journal article arguing that AI should be accepted into the publication and peer review process. #MedSky doi.org/10.1515/cclm-2025-1180
