Why text-to-image AI keeps failing at scientific figures (and what actually works)

Short summary

General text-to-image AI tools fail for scientific figures because they treat text as pixels (unreliable), can't support compositional edits, and use wrong visual styles. Structured-canvas tools that maintain figures as underlying data (boxes, arrows, labels) instead of pixels solve all three problems, cutting revision time from hours to minutes.

• •Text-to-image models hallucinate or misspell text because they render it as pixels, not characters—unsuitable for peer-reviewed papers
• •Pixel-based generation forces full redraws on reviewer feedback; structured tools allow 'add panel' edits that preserve layout, fonts, and colors
• •Structured-canvas approach (e.g., figcanvas) reduced author's workflow from half-day in Illustrator to 25 minutes, with revisions taking 10 minutes instead of two hours

Generated with AI, which can make mistakes.