GoKawiilArtificial-intelligence tools, when given well-written prompts, can generate engaging and clear graphical abstracts for research papers.Credit: Juj Winn/Getty
A graphical abstract in a paper is one of the most useful tools for communicating the hard-earned results of research. It makes findings more memorable, accessible and engaging for everyone, from domain experts and policymakers to curious readers outside the field.
But creating graphical abstracts isn’t easy, and not everyone has access to a graphic designer, or is comfortable with design software. Artificial-intelligence tools can help fill the gap — if they are used conscientiously.
Three tips to avoid AI image mistakes in science
Pre-AI, when I designed graphical abstracts, I spent significant time working out the dos and don’ts, which tools to use and how to begin. Now, I can use AI to dictate ideas through its speech-to-text functions, vectorize sketches, optimize colour palettes for accessibility, find references, brainstorm ideas and build custom tools to suit an exact workflow.
But with greater convenience comes greater responsibility. An AI-assisted graphic has your name on it, not the AI’s. It is on you, the user, to ensure that the image is scientifically accurate and neither plagiarizes nor infringes on others’ work.
So, how to create quality graphical abstracts while integrating AI responsibly and effectively? Here are some of the lessons I’ve learnt.
Think before you start
First, check your target journal’s guidelines on the use of AI. Some journals prohibit AI-generated images and others require a clear declaration.
Assuming that AI use is allowed, your next step is to work out what you want to communicate. Are you illustrating a mechanism, a process, an experimental set-up, a workflow, a comparison or a timeline? For one of my papers, the key message was how enzymes called deubiquitinases drive obesity-related liver pathology, and where in the disease process enzyme inhibitors would halt the progression or development of disease1. So, instead of mapping every individual molecular interaction, the graphical abstract — which in this case was created without AI — was designed to show how these enzymes contribute to disease progression using a single representative pathway rather than the full network of interactions identified in the literature. The goal was to provide enough context to make the mechanism intuitive and engaging, while leaving room for readers to explore the molecular details in the full study.
... continue reading