Dear OpenAI Team,
I’m writing to propose the development of a fully automated, scientifically accurate illustration generation tool that creates diagrams and visual content directly from structured or natural language input, with applications in:
- Anatomy & Medical Education
- Biology & Biochemistry
- Pharmacology and Systems Biology
- Environmental & Earth Sciences
- Engineering Schematics and Physics Diagrams
Why It’s Needed:
Current general-purpose models like DALL·E 3 and Midjourney can generate visually appealing images, but they:
- Lack biological or clinical accuracy
- Often hallucinate or distort anatomical structure
- Do not produce vector-editable or layered outputs needed for academic use
This creates a workflow bottleneck where AI-generated drafts must be manually cross-checked, corrected, and redrawn in tools like BioRender or Illustrator.
Proposed Features:
- Text-to-Diagram Capability: Accept structured prompts like “cross-section of kidney nephron with labeled glomerulus, proximal tubule, and loop of Henle.”
- Domain Accuracy: Trained or fine-tuned on expert-verified datasets (e.g., Netter, Gray’s Anatomy, KEGG pathways).
- Vector Output: Generate SVG or layered editable formats suitable for scientific publication.
- Optional Data Input: Accept raw biological data (e.g., gene expression profiles, molecular interactions) to automatically create pathway maps or expression heatmaps.
Impact:
- Drastically reduce time for educators and researchers to produce visuals
- Improve accessibility for students and clinicians without design backgrounds
- Expand OpenAI’s value in science, education, and knowledge communication
AM Fouda (MD, PhD)
Professor of Clinical Pharmacology and Applied Therapeutics
Mansoura University Faculty of Medicine, Mansoura 35516, Egypt