GenAI in scholarly authorship: the 2026…

The 2023 ICMJE position that generative AI cannot be a co-author has aged into a stable consensus across scholarly publishing, but the implementation surface around it has grown fast. In 2026 the question is no longer whether to disclose AI use in a manuscript; it is how, where, and with what evidence. This post maps the current disclosure landscape, the technical mitigations that publishers expect authors to apply, and the residual uncertainty around detection.

The ICMJE 2023 position and its echoes

In January 2023 the ICMJE updated its Recommendations to add that chatbots cannot be authors because they cannot meet the accountability criterion: an LLM cannot take responsibility for the integrity of the work, cannot approve the final version in any meaningful sense, and cannot be contacted by readers seeking clarification. The position was endorsed within weeks by the World Association of Medical Editors and by COPE. By mid-2023 every major publisher had aligned. The AI co-authorship rejection is now treated as a settled norm.

What replaced the brief flurry of “ChatGPT as co-author” papers was a more nuanced question: how should authors disclose AI use when the system is a tool? This is where 2024 and 2025 brought significant fragmentation, and where 2026 has begun to consolidate.

The publisher landscape in 2026

Nature and the Springer Nature stable

Nature requires authors to declare any use of LLMs in the Methods section (for research articles) or in the acknowledgements (for editorial and review content). The declaration must specify the model, the version, the date of use, and the purpose. Nature does not permit AI-generated images or figures except where the AI generation itself is the subject of the research. Springer Nature has cascaded a similar policy across its journals with light variation.

Cell Press and Elsevier

Cell Press and Elsevier journals require disclosure of AI-assisted writing in a dedicated declaration that sits alongside competing interests and funding. The declaration is structured: type of tool, purpose (e.g., language polishing, literature search, code generation, image analysis), and a confirmation that the authors take full responsibility. Elsevier additionally requires that AI-generated text be reviewed and edited by the authors and explicitly forbids using AI for peer review.

Wiley

Wiley’s policy distinguishes between using AI as a tool (allowed with disclosure) and using AI to generate substantive intellectual content (not allowed). The distinction is fuzzy at the boundary, and Wiley’s submission system asks authors to self-classify. Wiley also publishes its Best practice guidelines on research integrity and publishing ethics which were updated in 2024 to cover GenAI in detail.

PLOS, eLife, F1000Research

The open-publishing platforms have generally taken the position that AI use must be disclosed and that authors are responsible for verification, but they have been more permissive about disclosed and reviewed AI use than the closed-access incumbents. eLife in particular has experimented with AI-assisted peer review summaries, with disclosure to authors and readers.

What “disclosure” actually requires

The fragmentation across publisher policies has converged on a common five-element disclosure, which CASRAI’s AI disclosure helper assembles into a publisher-specific declaration:

Tool and version. Not “ChatGPT” but “GPT-4o (OpenAI), version of 2025-12-04.”
Purpose. One of: language polishing, translation, literature search, code generation, data extraction, image analysis, hypothesis generation, draft writing. If the use spanned multiple purposes, list each.
Scope. Which sections or artefacts were involved. “Abstract and discussion polished” is meaningfully different from “first draft written.”
Human verification. A statement that named authors have reviewed and verified the output and take responsibility for it.
Prompt and output retention. Increasingly, journals are asking authors to retain prompts and outputs for audit. Cell Press now formally asks; Nature recommends. Treat this as a 5-year retention obligation.

See our AI disclosure for authors guide for the publisher-by-publisher decision tree.

The hallucination problem

The single largest editorial concern in 2026 remains hallucination: an LLM fabricating a citation, a method, or a result and the authors failing to catch it. Retraction Watch tracked over 200 retractions in 2024 and 2025 attributable in whole or part to undisclosed AI-generated fabrications, primarily fictitious references but increasingly fabricated quantitative results in tables.

The mitigations are well-known and surprisingly under-applied:

Citation verification. Every citation in an LLM-generated draft must be checked against the actual source. Tools like Scite, Semantic Scholar’s citation graph, and Crossref’s metadata API help. The bare minimum: every DOI must resolve and the paper at that DOI must say what the LLM claims.
Numerical verification. If an LLM produces a number, the human author must reproduce the number from the underlying source. “The LLM said it” is not provenance.
Retrieval-augmented generation (RAG). Grounding an LLM in a fixed corpus of verified sources, with citation chaining, reduces but does not eliminate hallucination. RAG-based research-writing tools (Elicit, Consensus, scite Assistant) have an accuracy edge over raw LLMs precisely because they constrain the model to a verifiable corpus.

Munafò and colleagues at the UK Reproducibility Network have argued, correctly in our view, that AI-assisted writing should sit inside the same reproducibility envelope as the rest of the work: prompts and outputs are part of the methods, not part of the prose.

Detection and watermarking

The detection problem has not been solved. Tools that claim to identify AI-generated text by perplexity or burstiness have unacceptable false-positive rates against careful human writers and are easily defeated by simple paraphrasing. AI-assisted writing is, on the open web, essentially undetectable in 2026.

Three more promising directions exist. First, watermarking: the major LLM providers have prototyped statistical watermarks (Google’s SynthID-Text, OpenAI’s research-stage text watermark) that embed a detectable signal in token-selection statistics without affecting fluency. Adoption has been slow because authors can defeat watermarks by re-rolling with a different model, and because no publisher has committed to refusing un-watermarked submissions. Second, provenance metadata: the C2PA standard (originally for images) is being extended to text, with cryptographically signed assertions of generation source. Third, process auditing: rather than detecting AI in the output, audit the authors’ process artefacts (version history, prompt logs, draft trail). This is the direction in which institutional integrity offices are moving.

For authors, the practical takeaway is that you should not rely on undetectability. The conservative path is disclosure plus verification.

What about peer review?

The 2026 consensus is that peer reviewers may not paste unpublished manuscripts into a third-party LLM. The reason is confidentiality, not anti-AI sentiment: a paper under review is privileged information and most LLM providers retain inputs in some form. NIH and several large funders have made this an explicit policy for proposal review; publishers are catching up. eLife and a handful of others are experimenting with publisher-hosted LLM tooling that does not exfiltrate the manuscript, which threads the needle.

Where this is going

Three trajectories are visible. First, the disclosure form will converge: expect a NISO or COPE-led standardisation of GenAI disclosure within 18-24 months, modelled on the structured CRediT statement. Second, prompt-and-output retention will become mandatory for high-stakes journals (clinical, regulatory-relevant), and audited at random. Third, the line between “AI as tool” and “AI as substantive contributor” will be tested by hybrid systems where the human author’s contribution is curation, framing, and verification rather than generation. We expect the integrity community to draw a harder line on quantitative and methodological substance than on prose: an AI may polish your discussion section with disclosure, but an AI may not propose your analytic method without that proposal being independently validated and disclosed.

For now, the operating rule is straightforward. If you used AI, disclose it specifically. If the AI produced text or numbers in your paper, verify them yourself. If a publisher asks for prompts and outputs, retain them. If you are reviewing a paper, do not paste it into a chatbot. The GenAI disclosure domain at CASRAI tracks the publisher-by-publisher policy text for authors who need to comply across multiple submission targets.

References

ICMJE, Recommendations (January 2023 update, defining authorship to exclude AI). WAME, Chatbots, ChatGPT, and Scholarly Manuscripts (2023). COPE, Authorship and AI tools position statement (2023, reaffirmed 2025). Nature editorial, Tools such as ChatGPT threaten transparent science; here are our ground rules for their use (2023). Munafò et al., The reproducibility debate is an opportunity, not a crisis (PLOS Biology, 2022).