cs.CVMar 17, 2026

AI-Generated Figures in Academic Publishing: Policies, Tools, and Practical Guidelines

Authors: Davie Chen

Organizations: University of Arts in Poznań, Poznań, Poland

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Abstract

The rapid advancement of generative AI has introduced a new class of tools capable of producing publication-quality scientific figures, graphical abstracts, and data visualizations. However, academic publishers have responded with inconsistent and often ambiguous policies regarding AI-generated imagery. This paper surveys the current stance of major journals and publishers -- including Nature, Science, Cell Press, Elsevier, and PLOS -- on the use of AI-generated figures. We identify key concerns raised by publishers, including reproducibility, authorship attribution, and potential for visual misinformation. Drawing on practical examples from tools such as SciDraw, an AI-powered platform designed specifically for scientific illustration, we propose a set of best-practice guidelines for researchers seeking to use AI figure-generation tools in a compliant and transparent manner. Our findings suggest that, with appropriate disclosure and quality control, AI-generated figures can meaningfully accelerate scientific communication without compromising integrity.

Figures & tables

Publisher / JournalDRAKey Policy Points
Nature Portfolio✓Partial✓AI tools must be documented in Methods; AI-generated images must not misrepresent data
Science / AAAS✓Partial✓Text generated by AI must be disclosed; images policy follows general integrity guidelines
Cell Press✓Yes✓AI-generated figures must be clearly labeled; no AI in data figures without review
Elsevier✓Partial✓Disclosure required in cover letter and manuscript; AI cannot be credited as author
PLOS ONE✓Minimal✓Encourages transparency; no explicit ban on AI figures if properly disclosed
Springer Nature✓Partial✓Aligns with Nature Portfolio; emphasizes reproducibility
Table 1: Summary of publisher policies on AI-generated figures (as of January 2026). D = Disclosure required; R = Restrictions on AI-generated imagery; A = AI cannot be listed as author.
FeatureDALL·EMidjourneyStable Diff.SciDraw
Academic templates×××✓
Domain-specific styles×××✓
Text rendering qualityMediumMediumLowHigh
Iterative refinementLimitedLimitedYes (img2img)✓
Generation history×PartialManual✓
Metadata export××Manual✓
Table 2: Feature comparison of AI figure-generation tools for academic use.
Figure 1: A mechanism diagram illustrating the molecular mechanism of anti-tumor CAR-T cell therapy and a proposed novel strategy, generated using SciDraw’s mechanism diagram template. The figure demonstrates accurate rendering of molecular structures, signaling pathways, and domain-specific annotations. Source: SciDraw ( SciDraw, 2025 ) .
Figure 2: An experimental design diagram depicting a preclinical study with sample source, group allocation, timeline, and measurement endpoints, generated using SciDraw. Source: SciDraw ( SciDraw, 2025 ) .
Figure 3: A research framework diagram for a multidisciplinary study on digital economy-enabled rural revitalization, generated using SciDraw. The figure integrates research objectives, methods, and expected outcomes in a structured layout. Source: SciDraw ( SciDraw, 2025 ) .
Figure 4: An expected outcomes and impact diagram for a research grant proposal, generated using SciDraw. The concentric ring design organizes outputs from project core to societal impact across temporal scales. Source: SciDraw ( SciDraw, 2025 ) .
Figure 5: A technical roadmap for a multi-year research program on CAR-T cell therapy for solid tumors, generated using SciDraw. The figure integrates experimental aims, milestones, and project timelines in a structured visual format. Source: SciDraw ( SciDraw, 2025 ) .
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