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The dividing line is whether the published image still faithfully represents evidence that was actually obtained. Global adjustments that improve visibility can be acceptable when they are applied consistently and do not hide, remove, invent, or selectively emphasize scientific features. An AI-generated diagram that explains a workflow is a different category from a microscopy frame presented as experimental evidence. Generative AI must not be used to fabricate primary observations, and every journal has its own disclosure and submission rules.
What “manipulation” means in a scientific image
Image manipulation is not defined by whether a mouse, filter, script, or AI model was used. It is defined by the claim the image makes and whether processing changes that claim. A micrograph submitted as evidence says, in effect, “this is what the specimen or experiment produced.” If editing invents structures, erases observed features, changes the apparent strength of a signal, or makes one condition look different from another without scientific justification, the image no longer faithfully represents the evidence.
Processing can be legitimate when it preserves meaning. Researchers routinely adjust a display so faint structures can be seen, convert channels to a disclosed pseudocolor, or assemble a figure from multiple fields. The same operations become misleading when they are applied only to a favorable region, tuned differently for controls and treatment groups, or used to conceal a result that would alter a reader’s interpretation.
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Classify the image before deciding what edits are allowed
Publisher policies distinguish image categories because each makes a different evidentiary claim.
Explanatory diagrams and conceptual art
A workflow, pathway, cell cartoon, or conceptual diagram explains an idea rather than documenting a particular observation. Generative AI may be allowed for this purpose by some publishers, provided the figure is clearly illustrative and the tool, version, and use are disclosed where the journal requires. Do not style an AI illustration so that it could be mistaken for a real micrograph, and do not label invented structures as measured findings.
Data visualizations derived from measurements
A heat map, segmentation overlay, 3D reconstruction, or other visualization can be a valid representation of underlying measurements when the transformation is reproducible. Elsevier guidance permits AI-supported visualization only when it is directly derived from the underlying data through reproducible analytical, computational, or statistical methods and is described in Methods. The model must not invent or alter the underlying results. Keep the input data, code or settings, and processing record so another researcher can trace the output back to the measurements.
Primary research images
A microscopy frame, gel, radiograph, clinical photograph, or time-lapse clip presented as an observation is primary evidence. The reviewed Elsevier policy bars AI tools from creating or altering primary images that represent observed experimental data. The Journal of Clinical Investigation (JCI) states that generating or modifying data images with generative AI is strictly prohibited. JAMA prohibits AI-created or AI-manipulated clinical images and illustrations except when the AI operation is part of the formal research design or methods and is fully disclosed. A synthetic image cannot stand in for a specimen that was never observed.
How major policies differ
There is no single universal image rule. Compare the journal’s policy on four axes: the image category, the operation performed, the traceability of the evidence, and where disclosure must appear.
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| Image or operation | Guidance in the reviewed policies | Practical implication |
|---|---|---|
| AI-generated explanatory diagram or workflow | Elsevier permits certain explanatory images when the tool, version, and use are disclosed in the caption and in a general AI statement. | Use it only as illustration; identify it as synthetic and never present it as an observed result. |
| AI-supported data visualization | Elsevier permits visualizations directly derived from underlying data through reproducible analytical, computational, or statistical methods, reported in Methods. | Preserve a traceable path from input data to output and document the model and settings. |
| AI-generated or AI-altered primary microscopy image | Elsevier prohibits AI creation or alteration of primary images representing observed experimental data. JCI prohibits generation or modification of data images. | Do not replace observed pixels with generated ones. Seek the target journal’s specific exception, if any, for a formal method. |
| Uniform brightness, contrast, or color adjustment | JAMA allows uniform adjustments when they do not selectively highlight, misrepresent, obscure, or eliminate scientific elements. Nature Portfolio requires processing to be applied equally across the image and controls. | Apply the same justified operation to comparable samples and retain the original. |
| Pseudocoloring, gamma, threshold, or channel changes | Nature Portfolio says pseudocoloring and nonlinear changes should be disclosed; threshold manipulation, signal-range expansion or contraction, and altering high signals should be avoided. Individual channel adjustments may sometimes be necessary in merged images and should be noted. | Explain the transformation and its purpose; do not tune a display to favor a conclusion. |
| 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 is fully disclosed. | Keep provenance for every frame and consult the journal’s video rules before submission. |
When ordinary microscopy editing is acceptable
Uniform brightness, contrast, and color
A global brightness, contrast, or color adjustment can be acceptable when it is applied uniformly and does not selectively highlight, obscure, eliminate, or misrepresent scientific elements. “Uniform” means the operation is defined for the whole image or for a clearly stated, consistently treated set of images—not that you drag a slider until the desired area looks convincing.
Apply comparable settings to treatment and control images, all panels in a comparison, and the full field from which a representative crop was taken. If different exposure ranges or acquisition conditions require different displays, state that fact and provide the quantitative scale or acquisition information needed to interpret the panels.
Pseudocolor and channel display
Pseudocolor can make overlapping channels or intensity ranges easier to read, but the color is an encoding, not a newly observed property. State which channel received which color and whether the mapping is linear. If a merged image uses channel-specific adjustments, explain why. A color change that makes co-localization appear stronger than it is can alter the scientific conclusion even when no pixels were added.
Nonlinear changes, gamma, and dynamic range
Gamma correction and other nonlinear transformations change the relationship between measured intensity and displayed brightness. Nature Portfolio guidance calls for disclosure of nonlinear adjustments and advises avoiding threshold manipulation, signal-range expansion or contraction, and alteration of high signals. If a nonlinear display is necessary for visibility, preserve the raw data, record the exact transformation, and show a linear or otherwise interpretable reference when the result depends on intensity.
Cropping, stitching, and time averaging
A crop can be legitimate when it is representative and the figure makes the field of view clear. Do not crop away an edge, neighboring cell, damaged region, or failed replicate simply because it weakens the story. For stitched mosaics, indicate that fields were joined and retain the component images. For time-lapse footage or averaged frames, identify the time window and averaging method; a time-averaged image is not a single instant of observation.
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What is not routine processing
- Cloning, erasing, or painting over a cell, artifact, band, or background feature.
- Copying a structure from one region into another.
- Selective sharpening, brightening, blurring, or denoising of only the region supporting the hypothesis.
- Changing one experimental group’s display range so its signal appears comparable to, or stronger than, another group’s.
- Using a generative fill, inpainting, super-resolution model, or synthetic texture to supply details not captured by the instrument.
These operations can create or remove evidence. They are not made acceptable merely because the final image looks more attractive or easier to read.
Why AI enhancement is especially risky
Generative systems optimize for plausible appearance, not historical fidelity to a specimen. A model may infer a membrane, fill a gap, sharpen a repeated pattern, or remove noise by replacing measured pixels with statistically likely ones. The result can look scientifically reasonable while containing structures the microscope never recorded.
That risk is different from a deterministic visualization calculated from measurements. A segmentation algorithm that labels each pixel according to a documented rule can be audited against the source image. A generative model that invents texture or detail changes the evidentiary content unless the operation itself is the studied method and is fully disclosed.
For this reason, do not use generative AI to “clean up” a primary micrograph, reconstruct a missing field, increase apparent resolution for a claimed observation, or make a weak signal look continuous. If AI is part of the experimental method—for example, a model whose output is the object being tested—describe the model, inputs, training or validation procedure, version, parameters, and human checks in the methods section required by the journal. The generated output must still be identified as model output rather than an untouched observation.
A defensible image-handling workflow
Use this process for every figure and video, not only for images that look unusual.
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- Write the image claim. Record whether the item is an illustration, a visualization calculated from data, or a primary observation. Use a caption label that matches that claim.
- Preserve originals. Keep the unprocessed files, acquisition metadata, instrument settings, and the original video or frame sequence. JAMA may require original unedited photographs for editorial review; Nature Portfolio recommends retaining raw data and metadata.
- Record the toolchain. Log software and AI tool names, versions, plug-ins, scripts, settings, transformations, and export steps. Note who performed each operation and when.
- Define the processing rule before comparing groups. Specify which adjustments are global, which are channel-specific, and which are calculated from measurements. Apply the rule consistently to comparable images and controls.
- Check for removed or invented evidence. Compare the publication panel with the original at native resolution. Look for missing particles, repeated patterns, clipped highlights, suspiciously smooth backgrounds, or boundaries that appear only after enhancement.
- Validate calculated outputs. For segmentation, reconstruction, denoising, or other computational transformations, inspect representative failures as well as successful cases. Keep the source image beside the output so the reader or editor can assess the relationship.
- Write the disclosure while the record is available. State what changed, why it was necessary, which images were affected, and where the original data can be supplied. Follow the target journal’s requested location—caption, Methods, general AI statement, or submission files.
- Run the journal-specific check. Policies differ by publisher and may change. Confirm the current instructions for figures, supplementary files, video, AI use, and original-data requests before submission.
What a complete disclosure contains
Disclosure is not a substitute for faithful data, but it lets readers understand the transformation. The Centers for Disease Control and Prevention recommends five elements: “Content Affected + Action Taken + AI Tool + Purpose of AI Use + Human Oversight.” Adapt that structure to the journal’s format.
Routine processing statement
A useful caption identifies the operation and its scope, for example: “Brightness and contrast were adjusted uniformly across the entire field for display; no pixels were added or removed. The same procedure was applied to all treatment and control images.” Add channel mapping, nonlinear scaling, stitching, or averaging when those operations matter to interpretation.
AI illustration statement
For an explanatory figure, identify the tool and version, describe the purpose, and say that the image is illustrative rather than experimental evidence. If the publisher requests both a caption statement and a general AI declaration, provide both.
AI-assisted visualization or method statement
Describe the underlying data, model or software version, parameters, preprocessing, output type, validation, and human review. Make clear which pixels or values came from measurements and which are model-derived. Do not call a generated reconstruction a raw micrograph.
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Worked decisions for common scenarios
“I raised contrast so a faint organelle is visible.”
This can be acceptable if the adjustment is global, the organelle was present in the recorded data, the same rule is applied to comparable images, and the change does not clip or suppress other signals. Save the original and report the adjustment. If only one region was brightened, return to the source and reprocess the entire field or present the local enhancement as a clearly labeled analysis view rather than as the unqualified image.
“I used an AI denoiser on every frame.”
Uniform use does not by itself make a generative operation valid. Determine whether the tool merely applies a documented, reproducible filter or synthesizes likely detail. If it can create structures absent from the input, do not present the output as primary evidence unless the journal explicitly permits that operation as part of the formal method and you disclose and validate it fully.
“I colored a grayscale channel red and another green.”
Pseudocoloring is generally a display operation, but disclose the mapping and any channel-specific changes. Avoid colors or scaling that imply co-localization, intensity differences, or biological categories not supported by the measurements.
“I removed a bright dust particle from the field.”
Removing an artifact can change the reader’s assessment of image quality and signal distribution. Keep the untouched frame, explain the removal, and check the journal’s policy. If the particle could be confused with a biological feature or its removal affects a quantitative result, do not retouch the evidence; show the artifact and address it analytically.
“I generated a cell illustration for the introduction.”
This is explanatory art, not a primary image. It may be permissible under the publisher’s rules when labeled as illustrative and accompanied by the required tool, version, purpose, and oversight disclosure. Do not reuse it in a results panel or caption it as a representative micrograph.
Common failure modes and how to correct them
- Calling a synthetic image a representative image. Replace it with the actual observation or label the figure as an illustration or model output.
- Using different display limits for controls and treatment. Reprocess comparable images with a defined common rule, or disclose the reason and provide quantitative values.
- Relying on a screenshot as the only record. Preserve raw files, metadata, scripts, and settings; a flattened export cannot establish provenance.
- Disclosing only “AI used.” Name the affected content, action, tool and version, purpose, and human oversight, then place the statement where the journal requires.
- Assuming a journal’s allowance applies everywhere. Elsevier, JAMA, JCI, Nature Portfolio, and institutional guidance differ. Check the venue you will actually submit to.
- Showing only successful model outputs. Retain and evaluate failures, edge cases, and representative raw inputs so validation does not become a showcase of favorable examples.
- Overstating what processing proves. A clearer display does not create statistical significance, new resolution, or a biological structure that the instrument did not capture.
Submission checklist
- Is every figure labeled as an illustration, calculated visualization, or primary observation?
- Can each displayed feature be traced to an original acquisition or a documented calculation?
- Were adjustments applied consistently across the full image and relevant controls?
- Did any operation alter thresholds, dynamic range, gamma, channels, stitching, time averaging, or high-signal values? If so, is it documented?
- Were generative tools kept out of primary evidence unless they are an explicitly studied and fully disclosed method?
- Are raw files, metadata, software versions, settings, scripts, and intermediate outputs retained?
- Does the caption or Methods section state what changed, why, and how it was validated?
- Does the disclosure identify affected content, action, AI tool and version, purpose, and human oversight where AI was used?
- Have you checked the current figure, image, video, and AI policy of the intended journal?
The standard to apply
Ask whether the final image preserves the evidentiary meaning of the original observation. A transparent, reproducible display adjustment can improve communication without changing that meaning. Selective editing, fabricated detail, erased observations, or an unlabeled synthetic image crosses the line because it asks readers to treat something unobserved as evidence. When in doubt, show the original alongside the processed result, document every material transformation, and classify the image honestly.
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