Distinguishing between helpful denoising and outright fabrication requires moving beyond visual intuition. Researchers cannot rely on how 'clean' a micrograph looks. Instead, we need rigorous mathematical benchmarks. One approach involves using ground truth datasets generated via physical simulations or high-content scanning that captures the same biological structure at much higher photon counts. If an AI model produces features that deviate statistically from these known physics-based distributions, it is likely fabricating data.
We should also implement error-map protocols. For every enhanced image, the software must output a residual map—a side-by-side comparison showing exactly what information the algorithm added or removed. If the AI inserts structured patterns like membranes or organelles that do not exist in the
