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There is no single worldwide rule. Elsevier, JAMA, the Journal of Clinical Investigation (JCI), Nature Portfolio, the CDC and universities such as KU Leuven set related but not identical requirements. Classify the image by the claim it makes, preserve the original files and metadata, document every material operation, and check the target journal’s current instructions before submission.
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Start with the claim, not the software
The same application can produce either a legitimate figure or a misleading one. A reasonable reader should be able to tell which of these categories they are seeing:
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- Explanatory illustration: a schematic, workflow, conceptual drawing or artistic rendering made to explain an idea. It does not claim to be a photograph of a specimen.
- Data visualization: a calculated display derived from measurements, such as a segmentation map, heat map or reconstructed surface. Its inputs, algorithm and settings must remain traceable.
- Primary observation: a micrograph, gel, radiograph, clinical image or video frame presented as what the experiment recorded.
- Processed representation: an observed image that has been cropped, scaled, colored, averaged or otherwise transformed for display while retaining the evidence’s meaning.
Ask what a reader would infer without a caption. If the answer is “this is what the specimen looked like” or “this is what the microscope recorded,” the tolerance for alteration is much lower than for an illustration. Calling a synthetic image a “representative micrograph” does not make it one.
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What publishers generally prohibit
Across the policies reviewed, the clearest prohibition is fabrication of primary evidence. Elsevier says AI must not fabricate results, invent or alter underlying data, or generate figures that are not faithfully derived from the data and methods. JCI states that generative AI may not generate or modify data images. JAMA bars AI-created or AI-manipulated clinical images and illustrations unless the image is part of the formal research design or methods and the use is fully disclosed.
That means a generative model should not be used to:
- insert a cell, organelle, band, particle or other structure that was not recorded;
- remove a structure, blemish or field of view because it weakens the result;
- “hallucinate” detail into a low-resolution frame and then present the result as the microscope’s output;
- replace a damaged or missing frame with an AI reconstruction without making the replacement unmistakable;
- alter one treatment group, control or time point differently so that a contrast appears stronger;
- generate a clinical or experimental image and label it as a patient or specimen observation.
Formal methods research is the important exception: a study may deliberately test an image-generation or manipulation method. In that case the synthetic or altered material is the subject of the method, not undisclosed evidence for another biological claim. The method, training or input data, processing and validation still have to be reported under the journal’s rules.
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What routine microscopy processing can be acceptable
Microscopy software routinely changes how measured intensities are displayed. Such processing is not automatically misconduct, but it must not change the scientific meaning or hide information a reader needs to evaluate the result.
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Uniform brightness, contrast and color
JAMA permits uniform brightness, contrast or color adjustments when they do not selectively highlight, misrepresent, obscure or eliminate scientific elements. “Uniform” means the same operation is applied to the entire image area being shown, not that a brush or mask was used on only the favorable cells. Apply comparable settings to relevant controls and experimental groups; a different exposure or display range can create a false between-group effect.
Intensity ranges, gamma and thresholding
Nature Portfolio advises that microscopy adjustments apply to the entire image and says threshold manipulation, expansion or contraction of the signal range, and alteration of high signals should be avoided. Nonlinear changes such as gamma correction can be scientifically useful, but they must be disclosed because they change the relationship between displayed and measured intensity. If thresholding is part of a segmentation or counting method, report the thresholding algorithm and parameters in Methods rather than presenting the thresholded mask as an unqualified photograph.
Pseudocolor and channel operations
Pseudocoloring can make channels distinguishable, yet the colors are assigned by the author rather than recorded by the detector. Nature Portfolio expects pseudocoloring and nonlinear adjustments to be disclosed. Individual-channel adjustments may sometimes be necessary in a merged image; note which channel was changed, why it was needed and whether the same rule was used for all comparable samples. Never tune a color map solely to make colocalization or a boundary look convincing.
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A crop can be a legitimate figure layout choice when it is identified and the full field or an uncropped source is retained. Cropping becomes misleading when it removes a relevant control, an edge effect or an inconvenient region without notice. Stitching tiles, averaging time points and making projections can also be valid analytical operations, but the caption or Methods should state that the displayed image is a stitched, averaged, maximum-intensity or otherwise derived view rather than a single raw frame.
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AI-generated explanatory art versus AI-supported data visualization
Explanatory diagrams
Elsevier permits certain explanatory images when the caption identifies the AI tool and version and explains how it was used, alongside any required general AI statement. Label the result as a schematic, concept illustration or workflow. Do not use visual details that imply they came from your experiment unless those details are separately supported by actual data.
Visualizations derived from measurements
Elsevier allows AI-supported data visualizations only when they are directly derived from underlying data through reproducible analytical, computational or statistical methods and are reported in Methods. Keep the input data, code or model settings, transformation steps and validation outputs. A language or image model may help choose a layout or render a plot; it must not invent observations or silently modify the values being plotted.
Generative enhancement of a primary image
Super-resolution, inpainting, denoising or “detail enhancement” becomes a primary-image alteration when the model synthesizes pixels that were not measured. Even if the result looks plausible, a reader cannot distinguish measured structure from model prediction. Under the Elsevier, JCI and JAMA positions summarized above, do not submit such an output as the experimental micrograph. If a reconstruction is scientifically necessary, show the original beside it, identify the reconstruction as such, explain the algorithm and validate it against appropriate data; then follow the target journal’s specific policy.
Edited microscopy footage, video and audio
Video carries the same provenance obligation as a still image, with additional risks from selecting frames, changing playback speed or removing intervals. Keep the original acquisition file, timestamps and metadata. State whether the published clip is a continuous recording, a trimmed segment, a time-lapse, a projection, a stabilized sequence or a montage.
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Do not splice separate cells, fields or experiments into one apparently continuous event. If a montage is needed, use visible separators or labels and describe the assembly in the legend. Do not delete a frame showing a transient event merely because it disrupts the narrative; if frames were excluded for a technical reason, state the rule and its effect.
JAMA says AI-created or manipulated clinical video or audio is not permitted unless it forms part of the formal research design or methods and is fully disclosed. The same practical caution applies to microscopy footage: generated frames, synthetic transitions, AI interpolation and content-aware removal must never be implied to be recorded observations. Consult the target journal’s video instructions because the cited policies are examples, not a universal code.
A defensible workflow from acquisition to submission
- Define the image’s category. Write one sentence describing what the viewer is supposed to believe: observed specimen, processed observation, calculated visualization or illustration. Use that description in the figure legend where appropriate.
- Freeze the originals. Preserve raw image and video files, acquisition metadata, microscope settings, calibration information and the original file names. Make read-only archival copies and keep a record of who handled them. JAMA may require original unedited photographs for editorial review; Nature Portfolio recommends retaining raw data and metadata.
- Log every transformation. Record software and tool names, versions, plug-ins, settings, channel operations, crops, projections, stitching, frame exclusions and any AI model or service. KU Leuven advises that researchers be able to explain the software and tools used.
- Use global, reproducible operations first. Apply a documented adjustment to the full image or to a defined region using the same rule for comparable controls and conditions. Avoid local brushes, cloning, erasing and content-aware fills on evidence images.
- Separate analysis from display. Keep quantitative data and masks independent of a colorized presentation layer. If a threshold, segmentation, deconvolution or reconstruction generates the displayed result, preserve the input and parameter files and describe the method.
- Check for lost or invented information. Compare the submitted figure or clip with the original. Look for clipped highlights, crushed shadows, vanished structures, edge artifacts, duplicated objects, synthetic textures and inconsistent treatment between groups.
- Write the disclosure before submission. Put processing details in the legend or Methods where the journal directs. For AI use, identify the affected content, action, tool and version, purpose and human oversight. The CDC summarizes these elements as “Content Affected + Action Taken + AI Tool + Purpose of AI Use + Human Oversight.”
- Obtain an independent review. Have a colleague who knows the experiment but did not make the figure compare the processed output, original and processing log. Ask what they think the image depicts and whether any feature appears selectively emphasized.
- Follow the destination journal’s checklist. Policies change and differ by discipline. Check current instructions for image files, source-data submission, AI declarations, video supplements and retention periods immediately before submission.
How the major policy positions differ
| Image or operation | Guidance in the cited policies | Practical interpretation |
|---|---|---|
| AI-generated explanatory diagram or workflow | Elsevier permits certain explanatory images with tool, version and use disclosed in the caption and general AI statement. | Use it to explain an idea, and label it as an illustration rather than observed evidence. |
| AI-supported data visualization | Elsevier permits visualizations directly derived from underlying data through reproducible analytical, computational or statistical methods, reported in Methods. | Maintain a traceable path from inputs to output; the model cannot invent or alter results. |
| AI-generated or AI-altered primary microscopy image | Elsevier prohibits AI creation or alteration of primary images representing observed experimental data. JCI says generation or modification of data images is strictly prohibited. | Do not substitute synthetic pixels for measured evidence. |
| Uniform brightness, contrast or color | JAMA allows uniform adjustments that do not selectively highlight, misrepresent, obscure or eliminate scientific elements. Nature Portfolio expects equal treatment of image and controls. | Consistency helps, but it does not excuse an edit that changes meaning or hides data. |
| Pseudocolor, gamma, threshold or channel changes | Nature Portfolio says pseudocoloring and nonlinear adjustments should be disclosed; threshold manipulation and signal-range changes should be avoided. Necessary channel adjustments should be noted. | Explain what changed and why, and avoid tuning the display toward a preferred result. |
| AI-generated video or audio | JAMA does not permit AI-created or manipulated clinical video or audio unless it is part of formal research design or methods and fully disclosed. | Preserve provenance and consult the journal’s video rules; generated material must not appear to be recorded microscopy footage. |
These differences matter. JAMA is medical-journal focused; Elsevier separates explanatory images, visualizations and primary evidence; JCI takes a strict data-image position; Nature Portfolio gives detailed microscopy-processing guidance. Treat the table as a way to ask the right questions, not as a substitute for the destination journal’s instructions.
Worked examples: acceptable, questionable and unacceptable
Example 1: A dim fluorescence channel
You apply one linear brightness and contrast adjustment to every pixel in the field and to all matched controls, retain the raw 16-bit files and state the adjustment in the legend. This is generally consistent with JAMA’s uniform-adjustment principle, provided no signal is clipped or hidden.
Example 2: A “cleaned” background
You clone out dust spots and a bright object near the nucleus from only the treatment images. Even if the object is an imaging artifact, the local removal changes what was recorded and creates unequal processing. Keep the original, explain the artifact and use a transparent mask or a separate artifact-corrected analysis rather than silently erasing it.
Example 3: AI super-resolution
A model enlarges a low-resolution micrograph and draws plausible membranes. The enlarged image is a synthetic reconstruction, not a microscope observation. It cannot be used as the sole primary figure. If the reconstruction is central to a methods paper, show the original, identify the model output, provide validation and disclose the complete workflow.
Example 4: A conceptual cell illustration
An AI image depicts a signaling pathway for a graphical abstract and is clearly labeled as conceptual. You record the tool and version, describe its role in the caption and provide the required general disclosure. It is not evidence and must not be described as a representative micrograph.
Example 5: A time-lapse montage
You combine three recordings into a labeled panel to compare stages. Keep each source clip, label the montage and state that the clips are separate acquisitions. Do not present the montage as one uninterrupted event.
Failure modes and how to correct them
- “The software did it automatically.” Automation does not transfer responsibility. Identify the operation, inspect the result and retain settings.
- “The image is only for illustration.” If it is placed in a results figure or captioned as representative data, readers will treat it as evidence. Move it to a clearly labeled schematic or replace it with an actual observation.
- “We used the same tool, so it is fair.” The same tool can produce unequal masks, thresholds or exposure ranges. Compare the actual operations and outputs for every group.
- “We disclosed AI in the acknowledgments.” A generic acknowledgment may not satisfy a journal that requires a caption or Methods statement. Use the venue’s specified location and include tool, version, purpose and human review.
- “The original is on a hard drive.” An inaccessible or undocumented file is not a reproducible record. Preserve metadata, processing logs and a stable naming scheme, and be ready to supply originals to editors.
- “The change only improves aesthetics.” Aesthetic changes can alter apparent intensity, boundaries or object counts. If a viewer could draw a different scientific conclusion, treat the operation as material and disclose it.
Submission checklist
- The legend identifies whether the item is an illustration, calculated visualization, processed observation or primary image.
- Raw image or video files and acquisition metadata are retained and can be supplied if requested.
- Processing is applied consistently to comparable images and controls.
- No structure has been generated, cloned, erased, selectively hidden or selectively emphasized in a primary evidence image.
- Thresholding, gamma, pseudocolor, channel changes, stitching, projections, averaging, frame exclusions and other material operations are described.
- AI use, if any, names the affected content, action, tool and version, purpose and human oversight, in the location required by the journal.
- Any synthetic or reconstructed output is visibly and textually distinguished from recorded data.
- The current instructions of the destination journal have been checked for image, source-data, AI and video requirements.
Bottom line for authors and reviewers
Image integrity is a claim-to-evidence problem. If a pixel, frame or color carries information about what was observed, do not let a generative system invent it or an editor quietly remove it. Use ordinary processing only when it preserves meaning, apply it consistently, keep the untouched record and disclose material transformations. For explanatory art and reproducible visualizations, AI can be permissible, but the label, provenance and disclosure must prevent a reader from mistaking an illustration or model output for experimental evidence.
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