The Vancouver Standard: What Medical Researchers Need to Know About the Proposed Global AI Disclosure Framework

The gap between AI use and AI disclosure in medical research has been visible for several years, but 2026 is the year the field is actually trying to close it. The 9th World Conference on Research Integrity, held in Vancouver from May 3 to 6, produced something the research community has been calling for since large language models went mainstream: a coordinated multinational effort to build a single, universal standard for how AI use is reported in research. The draft framework that emerged, already known informally as the Vancouver Standard, is currently in its second public consultation phase, which runs through October 2026. Medical researchers who work under today's patchwork of journal-level policies should be paying close attention, even if the final document is still months away.

This is not a journal policy or a publisher announcement. It is a proposed standard backed by COPE (Committee on Publication Ethics), the International Science Council, the STM Association, the Global Young Academy, and the World Conferences on Research Integrity Foundation together. That combination of research-funder bodies, integrity organizations, and publishers means that when the Vancouver Standard is finalized, it is likely to filter into journal author instructions, institutional research policies, and grant-reporting requirements at roughly the same time, the way the Singapore Statement on Research Integrity eventually shaped institutional codes of conduct across the world.

How the WCRI Works, and Why Vancouver Mattered

The World Conference on Research Integrity has met roughly every three years since 2007. Each conference has produced an influential statement that gradually became a reference point for universities, funders, and journals negotiating what research integrity means in practice. The Singapore Statement (2010) established shared principles for research integrity across borders. The Hong Kong Principles (2019) focused specifically on researcher evaluation and how incentive structures distort research conduct. These documents do not have legal force, but they travel through institutional policy with surprising efficiency, often appearing verbatim in codes of conduct, grant agreements, and author guidelines within a few years of publication.

Vancouver 2026 was convened around three themes: artificial intelligence, research security (which covers foreign interference, dual-use research, and institutional risk management), and Indigenous knowledge systems. The AI theme dominated the program, which was not a surprise given how rapidly AI use had spread across medical research since 2022. One analysis cited at the conference found that only 5.7 percent of authors disclosed any AI use in their manuscripts, even at journals that explicitly required disclosure. That number, sitting alongside the explosion in AI-assisted research and writing, made the case for a new standard more clearly than any argument could.

The conference organizers ran a dedicated focus track on AI disclosure, bringing together stakeholders from the major medical publishers, research funders, and integrity bodies to work toward a draft standard. The result was the Vancouver Standard, named for the host city following the tradition of previous WCRI outputs, and it was sent into public consultation starting in July 2026.

The Problem That Made a Single Standard Necessary

The disclosure gap of 5.7 percent is striking, but the deeper problem is not that researchers are hiding AI use out of bad faith. Most of the time, the failure to disclose reflects genuine confusion about what should be disclosed, where it should go, and whether the journal requires it or just recommends it. A researcher who writes a discussion section using a chatbot, then cleans it up thoroughly, may not be sure whether their journal considers that authorship-level AI use or routine editing. A clinician who uses an AI tool to extract variables from electronic health records for analysis may not know whether that belongs in the Methods or the Acknowledgements or whether it needs to be mentioned at all.

The policies that exist are genuinely inconsistent. The ICMJE recommends disclosure at submission and in the manuscript body, with different sections depending on whether the AI was used in analysis (Methods) or writing (Acknowledgements). COPE offers principles rather than prescriptions. Individual journals layer their own rules on top, and flagship journals like JAMA, the New England Journal of Medicine, and The Lancet have each issued standalone AI policies with their own specific phrasing and requirements. Medical specialty journals sometimes diverge further still from their parent publishers' baseline policies. A researcher submitting to four journals in a year might follow four different disclosure protocols.

The Vancouver Standard's ambition is to replace that inconsistency with a shared framework, so that the question "did I disclose AI use correctly?" has a consistent, cross-platform answer rather than requiring researchers to re-read author guidelines for every target journal.

The Five Questions the Consultation Is Asking Right Now

The second consultation phase, which runs through October 2026, is organized around five unresolved questions in the draft. Understanding these questions tells you both where the standard is settled and where it remains genuinely open.

The first question is about the threshold for disclosure: when does AI use become significant enough to require reporting? The draft proposes that disclosure applies when AI use "substantively influences the research process, interpretation, reported content, or results." That language is intentionally broad, and the consultation is asking stakeholders whether it is workable in practice. For medical researchers, the threshold question has immediate implications. Does running patient data through an AI triage tool count? Does using a large language model to summarize a literature search qualify? The proposed answer in the draft is yes to both, because both influence interpretation and results, but that answer is still being refined.

The second question concerns taxonomy: how should different types of AI use be categorized? The draft proposes a classification of AI tasks into research design, data collection, data analysis, manuscript writing, reference identification, and peer review assistance. This taxonomy structure is meant to work like the CRediT author contribution taxonomy, where each contributor's role is mapped against a standardized vocabulary. A single taxonomy would allow journal submission systems to capture AI use in structured fields rather than relying on free-text author statements.

Third is placement: where in an article should the disclosure appear? The consultation is asking whether a standalone AI Use Statement should be a required element of every submission, separate from both the Methods and the Acknowledgements. Some publishers already use this model, but many do not, and the placement rules are not uniform. The draft leans toward a mandatory standalone section, comparable to a Conflict of Interest statement, so it travels with the paper through revision and remains findable in the published record.

Fourth is the mandatory question: should journals be required to collect a non-empty disclosure statement from every author at submission, even if the statement simply confirms that no AI tools were used? This would close the ambiguity problem where a missing disclosure could mean either "AI was not used" or "AI was used but not reported." The ICMJE has moved in this direction, but no universal requirement exists yet.

Fifth is accountability: what information must a disclosure statement contain to be meaningful? The draft proposes that a compliant disclosure should name the specific tool and version, describe the task it was applied to, confirm what human verification was performed, and include the authors' statement of responsibility for the output. That is more specific than most current journal requirements, which often accept generic phrases like "AI was used to improve readability." The Vancouver Standard would require researchers to say which tool, in which section, and how the output was reviewed.

How the Proposed Threshold Applies to Medical Research Tasks

The threshold question is where the draft standard will have the most practical effect on medical researchers, because medical research involves a wider range of AI-assisted tasks than most fields. A cardiologist using an AI model to identify arrhythmia patterns in ECG data before enrolling patients in a trial is doing something qualitatively different from a PhD student asking a chatbot to tighten the prose in a discussion section. The Vancouver Standard's draft would require disclosure for both, but the required disclosure statement would look very different.

For AI used in data collection and analysis, the draft standard would require Methods-level disclosure with enough detail that a disciplinary peer, meaning a clinician or methodologist with relevant expertise, could assess whether the AI's contribution might have affected the reliability of the reported findings. That is a higher bar than most current author guidelines impose. It means researchers need to report not just that they used an AI tool, but how it was configured, what validation was performed, and what the known limitations of the tool are in the relevant clinical context.

For AI used in manuscript writing, the proposed standard would maintain the current principle that AI tools cannot be listed as authors and cannot fulfill the accountability role of authorship. But it would extend the disclosure requirement to include any rewriting, paraphrasing, or translation that substantively altered the meaning or structure of text, not merely corrected grammar. That interpretation would capture most modern academic writing assistant tools, including Paperpal, Writefull, and Trinka when used beyond basic style correction, as well as general-purpose chatbots like ChatGPT and Claude.

How This Compares to Existing Guidelines

The Vancouver Standard is being developed alongside, not instead of, existing frameworks. The ICMJE's January 2026 revision added a standalone section on AI in publishing and requires disclosure at submission. COPE has issued principles on AI and authorship. Individual publishers have written their own policies, some detailed (JAMA Network's August 2026 update explicitly prohibits AI-generated citations, AI-generated clinical images, and AI-authored opinion pieces), some minimal.

The GAMER Statement (Guidelines for Accurate and Transparent AI Use in Medical Research), published earlier in 2026, addresses AI disclosure specifically in medical manuscripts and has already been endorsed by several clinical specialty journals. The Vancouver Standard differs from GAMER in scope: while GAMER focuses on the manuscript, the Vancouver Standard covers the entire research lifecycle, from study design through data collection and analysis to publication. A researcher who complied with GAMER might still need to add research-phase disclosures to satisfy the Vancouver Standard once it is finalized.

The CRediT taxonomy comparison is worth understanding. CRediT (Contributor Roles Taxonomy), which was developed collaboratively and is now embedded in submission systems at most major publishers, works by giving each author a standardized vocabulary of contribution types that travel with the paper as structured metadata. The Vancouver Standard draft envisions a similar integration for AI use disclosures, so that a structured AI Use Statement becomes metadata rather than a paragraph buried in the acknowledgements. That would eventually allow indexing databases to filter for AI-assisted studies, and might allow systematic reviewers and meta-analysts to screen for specific types of AI use when evaluating a body of evidence.

What Medical Authors Should Do While Waiting for the Final Standard

The Vancouver Standard is not yet final, and there is no enforcement mechanism until it is adopted by journals and institutions. For authors preparing manuscripts today, the practical guide is straightforward: follow your target journal's current author instructions and the ICMJE's published recommendations, which represent the current best consensus. The Vancouver Standard, when finalized, is likely to be more specific than current ICMJE guidance rather than contradicting it, so researchers who follow ICMJE now should need only modest additions once the new framework arrives.

The most useful preparation is to start treating AI disclosure as a prospective activity rather than a retrospective one. Before you begin any AI-assisted step in a research project, write a brief note describing the tool, the task, and the date. That record does not need to be formal, but having it means that when you write the eventual disclosure statement, you are working from a log rather than memory. Teams with multiple authors should make this a shared practice, because the corresponding author is often not the person who ran a machine learning pipeline or used a chatbot to translate the results section.

Researchers submitting to journals that already require standalone AI use statements (Elsevier's journals updated their submission systems earlier in 2026 to include a dedicated AI Use Declaration field, and several Springer Nature journals did the same) should treat those submissions as practice for the Vancouver Standard format. Use the five-element structure that the draft proposes: tool name and version, task description, section of the research or manuscript affected, verification method, and authorship responsibility statement. That format meets every current requirement I am aware of, and it is likely to be at least as specific as whatever the final standard requires.

How to Follow the Consultation Process

The second public consultation runs through October 2026. It is open to researchers, editors, publishers, funders, and institutional administrators. The International Science Council's AI Disclosure in Research project page (council.science) hosts the consultation documents and submission portal. COPE has also published a companion summary at publicationethics.org. If your institution has a research integrity officer or a research governance office, they may be organizing a collective response, which is worth contributing to, because input from clinical and medical research communities is particularly important for the sections on AI in data collection and analysis, where the medical field's use cases differ substantially from computing or social sciences.

WCRI 2026 organizers have indicated that a third consultation on a full draft of the Vancouver Standard is expected before the end of 2026, with the final version arriving in late 2026 or early 2027. The timeline is ambitious. For comparison, the CRediT taxonomy took roughly three years from initial development to widespread journal integration. The Vancouver Standard proponents are targeting a faster adoption cycle by designing for integration with existing infrastructure from the start, rather than expecting journals to retool their submission systems from scratch.

What the Adoption Cycle Might Look Like

If the Vancouver Standard follows the pattern of previous WCRI outputs, adoption will be uneven at first and then consolidate. The Singapore Statement took several years to move from conference declaration to widespread university policy. But the context has changed. The AI disclosure problem is more visible, the pressure from funders and regulators is more direct, and the organizations behind the Vancouver Standard are already embedded in journal submission infrastructure in a way that conference statement authors in 2010 were not.

COPE has 12,000 member journals, and when COPE recommends a practice, it reaches a large fraction of medical and scientific publishing. If the final Vancouver Standard is endorsed by COPE, the ICMJE, and the major publishers simultaneously, adoption could be meaningfully faster than prior standards. Researchers who are not currently reading the Vancouver Standard consultation documents should at least be aware that by mid-2027, the question of whether you disclosed AI use correctly may have a much more specific technical answer than it does today.

For medical researchers specifically, the practical implication is that AI use at any stage of a study, not just manuscript preparation, may soon need to be documented in a structured disclosure statement. That is a more demanding requirement than most researchers currently expect. The time to build a disclosure habit is before the requirement becomes mandatory, not after a desk rejection or a post-publication query asks why there was no AI Use Statement on a paper that clearly used AI.

A Broader Shift in How Research Accountability Works

It is worth stepping back and noting what the Vancouver Standard actually represents, beyond the practical implications for manuscripts. For most of medical publishing history, the accountability question was about authorship: who did the work and who stands behind the claims. AI has complicated that question in ways the existing authorship criteria were not designed to handle. When an AI tool designed the study protocol, another one analyzed the data, and a third drafted the paper, the human authors remain responsible in every current framework, but the accountability claim is more strained than it was when the same authors could point to their personal contributions at each step.

The Vancouver Standard's approach is not to redefine authorship or to restrict AI use. It is to make the AI contribution visible, specific, and verifiable, so that a reader, an editor, or a future systematic reviewer can assess the role that AI played and decide how to weight the findings. That is a reasonable response to a genuinely novel problem. It treats disclosure as the mechanism for maintaining accountability rather than trying to restrict the tools themselves, which no policy body has any practical ability to enforce.

Medical researchers who start now, documenting AI use prospectively, structuring disclosure statements around the five elements the draft proposes, and keeping pace with the consultation process, will find the transition to the final standard much less disruptive than those who wait. A disclosure habit is easy to build in stages. It is much harder to retrofit across a research program that has been running for two years.

Further Reading