Establishing these boundaries requires moving beyond vague ethical guidelines toward technical, verifiable requirements for data provenance. First, journals must mandate the disclosure of every computational step used during image processing. If a researcher applies a Generative Adversarial Network (GAN) to clean up a micrograph, they must specify the training dataset and the exact hyperparameters used.
We need a clear technical threshold. Legitimate denoising aims to restore known signal patterns by suppressing stochastic background noise. In contrast, fabrication introduces new structural information—like cellular organelles or protein clusters—that was never present in the original raw data. To catch this, reviewers should demand the submission of raw, unprocessed files alongside the AI-enhanced versions. Discrepancies between the raw signal-to-noise ratio (SNR) and the final output can reveal when an algorithm has hallucinated features.
Standardized benchmarks will also help. If we use the same gold-standard datasets to test noise-reduction algorithms, we can see exactly where an AI stops cleaning and starts inventing. Regulators should focus on the mathematical fidelity of the transformation. If the algorithm alters the pixel intensities in a way that violates the physical principles of the microscopy method used—such as fluorescence or electron diffraction—it should be flagged as deceptive.
