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Not without checking them. An AI-generated scientific visualization should be treated as unverified until its data, labels, transformations, and interpretation have been checked against the underlying evidence. A figure can look convincing and still be wrong. Knowing that AI was involved—or seeing a provenance label—does not establish that the science is accurate.
What to check before trusting a figure
Follow the evidence from its origin to the claim a reader is asked to accept. Each link can introduce errors, so verify each one rather than judging the image by how plausible it looks.
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- Source data: Confirm that the values or observations shown match the original dataset or another authoritative source.
- Transformation: Check calculations, filtering, aggregation, image editing, and any other processing applied between the source and the figure. For analytic or methodological uses, retain enough information about inputs, prompts, and settings to make the work reproducible when possible and safe.
- Visual encoding: Inspect every axis, scale, label, legend, unit, category, and depicted relationship. Look for omissions or choices that could change the apparent meaning.
- Caption and interpretation: Confirm that the caption describes the figure accurately and that the conclusion drawn from it follows from the evidence. Verify cited references and claims independently.
These checks put CDC guidance on accuracy, integrity, validation, and reproducibility into practice; the four-part evidence chain is a practical framework, not a formal standard quoted by CDC. CDC says authors should investigate questions about the accuracy or integrity of any part of their work and carefully review AI output. Its guidance quotes Morbidity and Mortality Weekly Report author instructions: “Authors should carefully review and edit the result, because AI can generate authoritative-sounding output that can be incorrect, incomplete, or biased.” CDC’s guidance on disclosing generative AI use in scientific work was reviewed May 28, 2026.
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What provenance can—and cannot—tell you
Provenance records, labels, and watermarks can help communicate a digital item’s origin or processing. They do not certify that plotted values are correct, that a scale is appropriate, or that the interpretation is sound. A figure with clear AI disclosure can still misrepresent data; a figure that accurately depicts data may still require disclosure under the applicable policy.
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NIST’s November 20, 2024 report surveys technical approaches to authenticating and tracking provenance, labeling synthetic content, detecting it, testing tools, and auditing content. Those approaches address transparency about content and its handling—not scientific validity. The report does not provide a general accuracy rate for AI-generated scientific visualizations. NIST AI 100-4, Reducing Risks Posed by Synthetic Content, was written by Bilva Chandra, Jesse Dunietz, Kathleen Roberts, Yooyoung Lee, Peter Fontana, and George Awad; its page was updated April 8, 2026.
How to disclose AI’s role
Disclosure should make the tool’s contribution understandable, not imply that it validated the result. CDC’s May 2026 guidance recommends identifying the tool or platform, model type and version when available, where and why it was used, and the extent of human oversight. For visual content, it recommends a visible watermark or label paired with accessible text in the caption, alt text, transcript, or an adjacent note. For analytic or methodological uses, document prompts, settings, inputs, and validation steps in enough detail to support reproducibility when possible and safe.
CDC offers this example wording, with placeholders to replace with accurate details: “Figure 1 was created using [Name of AI tool] [model/version, if available] [(manufacturer, location)]; authors checked all results for accuracy.” The final clause should only be used if authors actually performed the stated checks. See CDC’s disclosure guidance for its recommendations and examples.
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Journal and institutional requirements differ and can change. Before submitting, check the current policies for both the publication and your institution. Compare them on these points:
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- Whether AI-generated visual content is prohibited, permitted, or conditionally accepted.
- Where disclosure must appear and what details it must include.
- Whether the tool, model version, human review, or validation steps must be documented.
- What labeling is required for accessibility, such as a caption or alt text.
CDC reports that Emerging Infectious Diseases prefers not to publish AI-created figures, graphs, or images. That is one journal’s stated position, not a universal rule. NIH and HHS Office of Research Integrity staff also advise researchers to describe AI use, disclose image-editing processes, cite references accurately, verify claims, and consult institutional and journal policies. Their May 14, 2026 reminder warns that “Altering images with AI without full disclosure” may constitute data falsification. This guidance concerns NIH-supported research in the United States; it is not a finding that every AI image use is misconduct or a rule for every publisher and jurisdiction. Read the NIH and HHS Office of Research Integrity reminder.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.There is no established general accuracy rate
The official sources cited here provide process recommendations and discuss research-integrity risks; they do not establish how often AI-generated scientific visualizations are correct. No general percentage should be inferred from NIST’s review of synthetic-content transparency methods. Trust must come from checking the specific figure against its underlying evidence and following the relevant disclosure and policy requirements.
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