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Disclose AI use in a scientific image or microscopy video according to the journal you are submitting to. First identify what the visual represents, then name the AI system and version, explain its purpose and contribution, preserve the original evidence, and put the disclosure where that journal requires it—usually the figure caption or legend, an article-level AI declaration, and the Methods section when AI was part of the research method.
Policies are not interchangeable. Some publishers prohibit AI-generated or altered primary experimental images, while others permit narrowly defined, verifiable uses. A disclosure cannot make a prohibited manipulation acceptable, so check the destination journal’s current instructions before submission.
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Start by classifying the visual
The same software operation can have different implications depending on what the figure or video claims to show. A diagram explaining a process is not evidence of an observation. A plot calculated from measurements is not the same as an AI-generated micrograph. Classify the item before deciding what to write.
Conceptual or explanatory artwork
This includes schematics, workflow diagrams, graphical abstracts, and illustrations that explain a mechanism rather than record an observation. Some journals allow generative tools for this category with human oversight. The caption should identify the system, version, purpose, and the part of the artwork it produced or modified when the journal requires that information.
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Data visualizations
A chart, heat map, segmentation overlay, or other visualization derived from numerical data should remain traceable to the underlying dataset and reproducible from the stated procedure. Do not describe a visualization as “AI-generated” without explaining whether AI only selected a display format, performed an analysis, or created pixels that are not directly calculated from measurements. Elsevier’s policy, for example, requires data visualizations to be directly derived from underlying data and reproducible: Generative AI policies for journals.
Primary experimental images
A raw or processed micrograph that records an experimental observation is evidence-bearing. Adding structures, removing objects, moving features, inpainting damaged areas, or otherwise changing what appears in the field can alter the evidentiary record. Several publishers prohibit AI from fabricating or altering such images, even if the change makes the figure clearer.
AI used as part of the research method
AI may be part of the experiment or analysis itself—for example, a model that segments cells, classifies microscopy fields, detects particles, or controls an acquisition workflow. This is methodological use, not merely manuscript decoration. Describe it in the Methods with enough information for another researcher to reproduce the operation, and disclose it in any additional location the journal specifies.
Microscopy videos
Decide whether the video records experimental data, demonstrates a method, or combines recorded frames with explanatory graphics. A time-lapse showing cellular movement is evidence-bearing; a narrated animation of that movement is explanatory. Frame interpolation, denoising, stabilization, color mapping, object tracking, and generated transitions each need to be described accurately. Video rules may be stricter or broader than still-image rules: Springer Nature expressly includes video, JAMA guidance covers clinical video and audio, and Thieme generally bars generative-AI-created videos.
How publisher policies differ
The following comparison reflects the official policies available on October 13, 2026. A journal may add instructions in its author guidelines, submission system, or editor correspondence, so treat the journal’s current page as controlling.
| Venue | Visual boundary | Where and what to disclose |
|---|---|---|
| Elsevier journals | Certain explanatory images may be allowed with oversight. Visualizations must be derived from underlying data and reproducible. AI must not fabricate results or create or alter primary observed or experimental images such as microscopy. AI used as a research method may be allowed when reproducibly described. | Caption for an explanatory image, naming the tool, version, and use; a general AI declaration; Methods for data visualization or methodological use, including tool/model, version, and developer or manufacturer where applicable. Original unprocessed images or use documentation may be requested. Policy |
| Springer Nature | Policy covers figures, images, and video. Generation, analysis, or enhancement may be allowed when inputs are verifiable and attributed, output is accurate, human-reviewed, rights-compliant, and does not compromise the record. Opaque output without verifiable inputs is not permitted. | An AI Declaration naming the system, purpose, and extent of contribution, plus a specific caption or legend disclosure. Policy |
| JAMA Network | AI-created or manipulated clinical images and illustrations are not permitted unless they are part of a formal research design or method and are fully disclosed. The guidance also applies to clinical video and audio. AI data visualizations are permitted with software/tool name and version in the figure legend. | For content creation, revision, or formatting, an acknowledgment identifies the tool, version or extension, manufacturer, dates, task or portion affected, and author responsibility. Permitted image adjustments are described in the legend; original photographs may be requested for editorial review. Guidance published August 10, 2026 |
| JCI Insight | AI to generate or modify data images is prohibited. Other AI use in research and manuscript preparation must be disclosed. | A paragraph titled “AI disclosures” in Manuscript information, stating the task, tool name and version, and date or date range. The author guidance includes an example involving microscopy image analysis. Author Information Center |
| Thieme Science journals | Generative-AI-created images, videos, and illustrations are generally not permitted; experimental-material images must come from experiments. Non-generative machine-learning enhancement of existing images may be allowed under accepted practices. | A generative-AI declaration in the manuscript. Legitimate exceptions require contacting the publishing editor and obtaining approval. Caption disclosure is used for permitted non-generative enhancement. Journal Policies |
What the differences mean in practice
Permission at one publisher does not transfer to another. For example, an explanatory figure accepted by one venue may be disallowed by a journal that treats all generated illustrations as impermissible. If a policy is ambiguous, ask the handling editor before submission and keep the written response with your submission records.
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A disclosure workflow you can apply to every figure or video
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1. Identify the visual’s evidentiary role
Label each item as conceptual artwork, data visualization, primary experimental image, methodological output, or video. For video, note whether it records data, demonstrates a procedure, or adds explanation. This classification should appear in your internal figure inventory even when it does not appear verbatim in the manuscript.
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2. Read the exact journal policy
Check the journal’s author instructions and the applicable publisher policy immediately before submission. Look specifically for rules covering figures, microscopy, clinical images, video, audio, data visualization, and research-method AI. Do not rely on a general statement that “AI is allowed”; determine which category is allowed and under what conditions.
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3. Record the operation while you work
Maintain a log for every affected item. Record the system or tool name, exact version, developer or manufacturer when requested, date or date range, purpose, affected figure or video and panels, and the extent of the contribution. For a research method, preserve the inputs, preprocessing, relevant settings, model or algorithm configuration, and output-selection criteria needed to reproduce the result.
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4. Preserve and validate the evidence
Keep the source dataset, original unprocessed images, and original video files in their native formats. Compare the submitted output with those materials. Verify that AI did not invent, remove, obscure, duplicate, move, or selectively emphasize evidence. Human review is not a substitute for retaining the inputs; it is an additional check.
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5. Put each disclosure in the required location
Use a caption or legend for a specific figure or video when the policy calls for item-level disclosure. Use the journal’s manuscript-level AI declaration for broader use. Put methodological use in Methods, with a caption cross-reference when the same operation affects a figure. Follow the journal’s prescribed heading and submission-field names rather than placing every statement in acknowledgments.
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6. Make a truthful non-use statement only when appropriate
Some author pages provide examples of “no AI used” declarations. Use one only if no covered AI operation occurred and if its wording fits the journal. A non-use statement does not erase a separate obligation to describe conventional image processing or statistical software.
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What a complete disclosure should contain
Policies use different labels, but a reviewer should be able to answer five questions: what system was used, which version, why it was used, where it acted, and how much it contributed.
- System identity: Give the product, model, algorithm, or service name and exact version. Add the developer or manufacturer where the journal requests it.
- Timing: State the date or date range when the operation occurred if required by the venue or useful for version traceability.
- Purpose: Say whether the operation generated explanatory artwork, analyzed data, enhanced an existing image, produced a visualization, edited a video, or formed part of the research method.
- Scope: Identify the figure, panel, video, time interval, or subset of frames affected. State whether the tool produced content, changed existing pixels, classified objects, or only assisted layout or wording.
- Verification and responsibility: State that the authors checked the output against the original evidence and remain responsible for accuracy, when the journal’s format permits such a statement.
Caption or legend example
Adapt the following structure to the journal’s terminology; the double-curly fields are variables, not text to leave unresolved:
AI use: {{system_name}} {{version}} ({{developer_or_manufacturer}}) was used on {{date_or_date_range}} to {{purpose}} in Figure {{figure_number}}, panel {{panel}}. The authors compared the output with the original data and retain the unprocessed files.
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Manuscript-level AI declaration
A declaration can cover multiple figures or operations, but it should point readers to item-level captions when necessary:
AI disclosures: {{system_name}} {{version}} was used during {{date_or_date_range}} for {{task}}. It affected {{figures_or_video}} by {{extent_of_contribution}}. The authors reviewed the outputs, verified them against source material, and accept responsibility for the submitted content.
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Use the journal’s required heading—such as an AI Declaration, AI disclosures paragraph, or acknowledgment—rather than inventing a new section when the instructions prescribe one.
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Methods description for AI-assisted analysis
When AI is part of the study method, explain the acquisition or analysis pipeline in the same way you would document another instrument or algorithm. Identify the input data, preprocessing, model or tool and version, relevant parameters or decision thresholds, output used in the analysis, quality checks, and the way results were independently verified. Link the resulting figure or video to that Methods description.
Microscopy-specific decisions
Routine processing versus content-changing edits
Conventional adjustments such as uniform brightness or contrast changes may be treated differently from content-generating edits, but the journal’s image-integrity rules still apply. An AI denoiser that removes signal, a super-resolution model that synthesizes detail, or an inpainting tool that fills missing structures can change interpretation. Describe the operation rather than calling it simply “enhancement.” If the policy prohibits altering primary images, do not submit the altered image as the evidence-bearing panel.
Segmentation, counting, and classification
If a model identifies nuclei, tracks particles, counts colonies, or classifies cell states, the model output is part of the analysis. Report how it was applied and how errors were checked. A colored mask or boundary drawn from measured pixels is a data-derived visualization; a generated background or reconstructed object is a different category and may be prohibited.
Time-lapse and instrument videos
Keep the original frame sequence and acquisition metadata. State whether frames were dropped, interpolated, stabilized, denoised, re-colored, cropped, or annotated. If AI created transitions or objects that were not recorded, label the result as explanatory rather than experimental—or do not use it if the journal bars generated videos. Clinical videos and audio require particular attention under JAMA’s guidance.
Evidence retention and editorial review
Publishers may ask for the materials needed to inspect an image or reproduce the operation. Organize a verification package before submission:
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- Original, unprocessed micrographs and video files with acquisition metadata.
- The underlying numerical data used for plots, masks, tracks, or other visualizations.
- A versioned log of AI operations, including affected files and dates.
- Model outputs and, where feasible, the settings or prompts that produced them.
- A side-by-side record showing the submitted visual and its source material.
- Documentation of human checks, exclusions, and any disagreements resolved during review.
Elsevier notes that authors may be asked for original images or use documentation. Springer Nature requires verifiable inputs, accurate output, human review, and protection of the scholarly record. These requirements make traceability part of image preparation, not an administrative task added after acceptance.
Common failure modes and how to correct them
“We disclosed AI, so any image is acceptable”
Disclosure is not permission. Remove or replace a prohibited generated or altered primary image, or obtain written approval for a policy-defined exception before submission.
Only naming the software
A product name without a version, purpose, affected item, or extent of contribution leaves the operation ambiguous. Complete the disclosure fields and connect the statement to the relevant caption or Methods section.
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Calling generated pixels a visualization
A visualization must be traceable to data. If a model invented structures, textures, or intermediate frames, describe it as generated content and apply the journal’s rule for that category; do not present it as a direct display of measurements.
Putting methodological AI only in acknowledgments
AI that affects study design, acquisition, analysis, or interpretation belongs in Methods and the journal’s required declaration. An acknowledgment alone may not provide reproducibility.
Forgetting video-specific rules
Do not assume that approval for a still figure covers a microscopy video. Recheck provisions for video, audio, clinical footage, frame processing, and generated transitions.
Using a generic “no AI” sentence
A statement copied from another venue can be inaccurate or placed in the wrong section. Write only what is true for the submitted work and follow the destination journal’s wording and location requirements.
Final submission checklist
- Every figure and video has been classified by evidentiary role.
- The destination journal’s current AI, image-integrity, and video rules have been checked.
- Any prohibited generation or alteration of primary experimental imagery has been removed.
- Tool or system name, version, purpose, scope, and dates are recorded where required.
- Methodological AI is described reproducibly in Methods.
- Captions or legends identify affected visuals and permitted operations.
- The article-level declaration uses the journal’s required heading and location.
- Original images, source data, video files, and operation logs are retained.
- Authors have verified that output accurately represents the evidence and accept responsibility for the submitted content.
Accountability language from publisher policies
The policies place responsibility on authors, not on the software. Elsevier states that “Authors are responsible for ensuring the accuracy and originality of all images submitted for publication.” Springer Nature lists disclosure of the model and purpose among the conditions for visual content derived from verifiable inputs. JAMA likewise states that “Authors are responsible for the accuracy of all content submitted to the journal.” These statements reinforce the practical rule: identify the operation, preserve the evidence, and do not use disclosure to justify an image the journal does not permit.
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