The shift toward AI-enhanced microscopy forces a move away from mere visual inspection toward algorithmic verification. Traditionally, reviewers looked at micrographs to identify morphological features or cellular structures manually. Now, deep learning models often perform the segmentation and quantification. This creates a transparency gap. If a researcher uses a generative adversarial network (GAN) to sharpen an image, the line between enhancing signal and fabricating data blurs. Reviewers can no longer rely solely on their eyes to spot inconsistencies.
Consequently, the criteria for empirical evidence are changing. Journals may soon require authors to submit the raw, unprocessed data alongside the AI-processed versions. We might see a requirement for
