AI image generators are transforming scientific and technical visualization by enabling researchers to create complex diagrams, data representations, and illustrative graphics from text prompts. These tools leverage advanced neural networks to interpret detailed descriptions and produce high-quality visuals that can enhance clarity in publications, presentations, and educational materials. However, their use requires careful adherence to ethical guidelines, as most scientific journals prohibit direct submission of unedited AI-generated images. Instead, researchers must use these outputs as foundational templates for further customization to meet publication standards.

Key considerations for effective use include:

• Selecting specialized tools like Recraft, VectorArt.ai, or Adobe Firefly for vector-based scientific illustrations that can be edited in software like Illustrator ^[1]
• Crafting precise prompts that specify scientific details (e.g., "cross-section of a mitochondrion with labeled ATP synthase, electron transport chain, and membrane proteins, isometric view, scientific illustration style") to ensure accuracy ^[5]
• Verifying scientific accuracy through peer review, as AI-generated images may contain errors (e.g., the retracted Frontiers in Cell and Developmental Biology paper with inaccurate AI figures) ^[10]
• Understanding legal constraints, as copyright laws for AI outputs remain unclear, and journals often require disclosure of AI assistance ^[2][10]

Practical Applications and Workflow for Scientific Visualization

Selecting and Using AI Tools for Technical Accuracy

AI image generators vary significantly in their suitability for scientific applications. Tools designed for vector output—such as Recraft, VectorArt.ai, and Adobe Firefly—are particularly valuable because they produce scalable, editable graphics that can be refined in vector software like Adobe Illustrator ^[1]. These platforms allow researchers to generate base illustrations (e.g., molecular structures, anatomical diagrams, or data flowcharts) that can later be annotated, relabeled, or restructured to meet journal specifications.

For raster-based scientific visuals (e.g., microscopic images, 3D renderings of proteins, or simulation snapshots), DALL·E 3, Midjourney, and Stable Diffusion offer high-resolution outputs. However, their use requires:

• Prompt engineering with technical precision. For example, a prompt for a protein structure might include: "Ribbon diagram of CRISPR-Cas9 complex with DNA strand, PDB-style color coding (blue helices, green sheets, red loops), 8K resolution, transparent background, scientific journal style" ^[5]. Including references to existing scientific styles (e.g., "Nature Methods illustration") improves relevance.
• Post-generation validation. AI tools may misrepresent spatial relationships or molecular bonds. In 2023, a paper was retracted after AI-generated images depicted impossible cellular structures ^[10]. Researchers must cross-check outputs against established data (e.g., Protein Data Bank entries for molecular visuals).
• Layered editing. Most journals require manual adjustments to AI outputs. For instance, a DALL·E-generated neuron illustration might need relabeling in Illustrator to correct misplaced axon terminals or adjust color schemes to match journal guidelines ^[1].

Key platforms and their scientific applications:

• Recraft: Best for vector-based diagrams (e.g., experimental setups, flowcharts). Offers direct SVG exports for editing ^[1].
• Adobe Firefly: Integrates with Photoshop/Illustrator for seamless refinement. Includes "Generative Fill" to modify specific regions (e.g., adding labels to a graph) ^[2].
• DALL·E 3 (via Microsoft Designer): Free tier available; excels at photorealistic scientific reconstructions (e.g., fossil reconstructions, historical lab equipment) ^[9].
• Midjourney: Preferred for 3D-style renderings (e.g., viral capsid structures, nanotechnology visuals) but requires subscription ^[4].

Ethical and Legal Considerations in Scientific Publishing

The use of AI in scientific visualization introduces ethical and legal complexities that researchers must navigate to avoid retraction or reputational damage. Most journals—including Nature, Science, and Cell—require explicit disclosure of AI assistance and prohibit submission of unedited AI-generated images as original figures ^[10]. Key guidelines include:

• Disclosure requirements:
• Frontiers journals mandate authors to declare AI use in the Methods section, specifying the tool and prompt ^[10].
• PLOS permits AI-generated images only if "substantially modified" by the author and accompanied by raw data validation ^[10].
• Elsevier prohibits AI images in research articles but allows them in reviews or opinion pieces with clear labeling ^[1].

• Copyright and ownership ambiguities:
• AI-generated images exist in a legal gray area. U.S. Copyright Office rulings suggest they lack human authorship, making them ineligible for copyright protection ^[2].
• Journals may reject submissions if AI outputs resemble copyrighted works (e.g., an AI-generated graph mimicking a Nature figure’s layout) ^[7].
• Researchers should use Creative Commons-licensed datasets (e.g., BioRender templates) as prompts to mitigate risks ^[1].

• Scientific integrity risks:
• AI tools may fabricate plausible but incorrect details. For example, an AI-generated Western blot might show bands at impossible molecular weights ^[10].
• The Tri-I community report warns that AI use without oversight can lead to "reputational harm" and "loss of public trust in science" ^[10].
• Solutions include:
• Peer validation: Have domain experts review AI outputs before submission.
• Hybrid workflows: Use AI for drafts but recreate critical figures manually (e.g., tracing AI-generated sketches in Illustrator) ^[1].
• Journal pre-approval: Contact editors to confirm AI policies before submission.

Practical steps to ensure compliance:

1. Document the AI process: Save prompts, generation parameters, and editing steps for transparency.
2. Combine AI with original data: Overlay AI visuals with real experimental data (e.g., superimposing AI-rendered cell structures onto microscopy images).
3. Use journal-approved tools: Some publishers partner with specific platforms (e.g., Cell Press recommends BioRender over generic AI tools) ^[10].