From Lab Notebook to Figure: Practical Notes on Using AI Image Generation for Science Communication

Researchers increasingly need clear visuals: graphical abstracts, conference posters, outreach slides and teaching materials. Drawing every schematic by hand in vector software takes hours, and stock illustrations rarely match the specific biology you want to show. Over the past year, text-to-image models have become a realistic option for drafting concept art, background scenes and illustrative mock-ups before a final figure is polished. For Chinese-speaking labs and students, a localized tool such as ai生图 makes it easy to describe a scene in your own language and get several photorealistic drafts in seconds, which is useful when brainstorming how to visualize a cell, an organoid culture or a lab workflow.

A few practical guidelines help keep AI-generated images appropriate in a scientific context. First, never present a generated image as experimental data: microscopy, gels, blots and plots must always come from real measurements, and generated art should be labeled as illustrative. Second, check anatomical and molecular accuracy carefully, because models often invent structures, mislabel organelles or produce impossible protein shapes. Third, use generated images mainly as backgrounds, cover art or rough layouts, then redraw key scientific elements with accurate tools such as PDB-based renderers or BioRender-style templates.

Prompt writing matters too. Specify the viewpoint (cross-section, top-down, macro photo), lighting, color palette and the level of realism you want, and mention what to exclude, such as text artifacts. Iterating with small changes is faster than writing one huge prompt. Finally, review journal and conference policies, since many publishers now require disclosure of AI-assisted figures. Used responsibly, AI image generation can save time and make research stories more accessible to students and the public.