2026/03/17 by Davie Chen · 2 voices
Computer Science · Medicine · #Artificial Intelligence in Healthcare and Education #Class (philosophy) #Data Visualization and Analytics #Key (lock) #Multimodal Machine Learning Applications #Quality (philosophy) #Scientific literature #Set (abstract data type) #cs.CV #cs.CY
paper · pdf · doi:10.48550/arxiv.2603.16159
openalex publication_date 2026/03/17 · arxiv published 2026/03/17 · arxiv updated 2026/03/17 · openalex created_date 2026/03/20 · openalex updated_date 2026/07/28
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.