NEJM figure preparation: resolution (300-1200 DPI), editable vector formats (AI/EPS/SVG), in-house medical illustration policy, and strict image integrity requirements.
npx skills add https://github.com/jaechang-hits/SciAgent-Skills --skill nejm-figure-guide
This guide provides the complete specifications for preparing figures for submission to the New England Journal of Medicine (NEJM). A unique feature of NEJM is that medical illustrations are created by NEJM's in-house illustrators working directly with authors — authors should NOT submit finished medical illustrations due to copyright considerations.
Official reference: https://www.nejm.org/author-center/new-manuscripts
| Image Type | Minimum Resolution | Notes |
|---|---|---|
| Black-and-white line art | 1,200 DPI | Highest requirement |
| Photographic / halftone images | 300 DPI | Standard for photographs |
| Peer review stage | Lower resolution acceptable | High-res required for final publication |
from PIL import Image
def check_nejm_resolution(image_path, image_type='photo', stage='final'):
"""Check if image meets NEJM resolution requirements.
Args:
image_type: 'lineart' (1200 DPI) or 'photo' (300 DPI)
stage: 'review' (lower OK) or 'final' (strict requirements)
"""
min_dpi = {'lineart': 1200, 'photo': 300}
required = min_dpi.get(image_type, 300)
if stage == 'review':
print("NOTE: Lower resolution acceptable for peer review")
required = 150 # relaxed for review
img = Image.open(image_path)
dpi = img.info.get('dpi', (72, 72))
print(f"Stage: {stage} | Type: {image_type}")
print(f"Required: {required} DPI | Actual: {dpi[0]} DPI")
passed = dpi[0] >= required
print("PASS" if passed else "FAIL")
return passed
| Figure Type | Preferred Format | Notes |
|---|---|---|
| Data visualizations (graphs, plots, diagrams) | AI, EPS, SVG | Editable vector files preferred |
| Photographic images | TIFF | High-resolution raster |
| Medical illustrations | Do NOT submit | NEJM illustrators create these |
IMPORTANT: NEJM's in-house medical illustrators will work directly with authors to create medical illustrations. Authors should NOT submit finished illustrations due to copyright considerations. The journal retains copyright on illustrations created by their team.
NEJM does not publish detailed size specifications in their public guidelines. General best practices:
| Element | Specification |
|---|---|
| Preferred style | Sans-serif |
| Historical font | Univers (NEJM house font) |
| Alternatives | Helvetica, Arial |
import matplotlib.pyplot as plt
def set_nejm_fonts():
"""Configure Matplotlib for NEJM figure fonts."""
plt.rcParams.update({
'font.family': 'sans-serif',
'font.sans-serif': ['Univers', 'Helvetica', 'Arial'],
'font.size': 8,
'axes.labelsize': 8,
'axes.titlesize': 8,
'xtick.labelsize': 7,
'ytick.labelsize': 7,
'legend.fontsize': 7,
})
def check_clinical_image_text(title, legend):
"""Validate text limits for NEJM Images in Clinical Medicine."""
title_words = len(title.split())
legend_words = len(legend.split())
issues = []
if title_words > 8:
issues.append(f"Title has {title_words} words (max 8)")
if legend_words > 150:
issues.append(f"Legend has {legend_words} words (max 150)")
if issues:
for issue in issues:
print(f"ISSUE: {issue}")
else:
print(f"PASS: Title ({title_words} words), Legend ({legend_words} words)")
return len(issues) == 0
from PIL import Image
import os
def validate_nejm_figure(image_path, image_type='photo', stage='final'):
"""Full validation of a figure against NEJM requirements."""
img = Image.open(image_path)
issues = []
# 1. Resolution check
min_dpi = {'lineart': 1200, 'photo': 300}
required = min_dpi.get(image_type, 300)
if stage == 'review':
required = 150
dpi = img.info.get('dpi', (72, 72))
if dpi[0] < required:
issues.append(f"Resolution {dpi[0]} DPI below {required} DPI for {image_type} ({stage})")
# 2. Color mode
if img.mode not in ('RGB', 'RGBA', 'L'):
issues.append(f"Color mode {img.mode} may not be ideal; use RGB or Grayscale")
# 3. Format check
fmt = img.format
vector_preferred = image_type != 'photo'
if vector_preferred and fmt and fmt.upper() in ('JPEG', 'PNG'):
issues.append(f"Data visualizations: prefer vector format (AI, EPS, SVG) over {fmt}")
# Report
print(f"=== NEJM Figure Validation ({stage}) ===")
print(f"Dimensions: {img.size[0]} x {img.size[1]} px")
print(f"DPI: {dpi[0]} x {dpi[1]}")
print(f"Color mode: {img.mode}")
if issues:
print(f"\nISSUES FOUND ({len(issues)}):")
for issue in issues:
print(f" - {issue}")
else:
print("\nAll checks PASSED")
print("\nREMINDER: Do NOT submit finished medical illustrations (NEJM creates these)")
print("REMINDER: Remove ALL patient-identifying information from images")
return len(issues) == 0
NEJM's most distinctive policy is that medical illustrations are created by their in-house illustrators working directly with authors. Authors should NOT submit finished medical illustrations. The journal retains copyright on illustrations created by their team. This applies only to medical illustrations — data visualizations and photographs are author-submitted.
NEJM accepts lower-resolution figures during peer review to reduce submission friction. However, final publication requires full resolution: 1,200 DPI for line art and 300 DPI for photographs. Authors should prepare high-resolution originals from the start to avoid rework.
NEJM enforces strict patient privacy requirements. All patient-identifying information must be removed from images, including faces, names, medical record numbers, and any other identifiable features. This is non-negotiable and applies to all clinical images regardless of consent status.
What type of figure are you preparing?
├── Medical illustration (anatomy, mechanism)
│ └── Do NOT submit → NEJM illustrators create these
├── Data visualization (graph, chart, diagram)
│ ├── Vector source available → AI, EPS, or SVG (preferred)
│ └── Raster only → TIFF at 1,200 DPI (line art) or 300 DPI (photo)
├── Clinical photograph
│ ├── Patient identifiable? → Remove ALL identifying information
│ └── Ready for submission → TIFF at 300+ DPI
└── What stage?
├── Peer review → Lower resolution acceptable
└── Final publication → Full resolution required
| Scenario | Format | Resolution | Special Considerations |
|---|---|---|---|
| Kaplan-Meier curve | AI, EPS, SVG | Vector | Editable format preferred |
| Clinical photograph | TIFF | 300+ DPI | Remove patient identifiers |
| Histology image | TIFF | 300+ DPI | Include scale bar |
| Flowchart or diagram | AI, EPS, SVG | Vector | Use Univers or Helvetica |
| Medical illustration | Do NOT submit | N/A | NEJM creates in-house |
| Images in Clinical Medicine | TIFF/JPEG | 300+ DPI | Title max 8 words, legend max 150 words |
Before submitting figures to NEJM, verify:
Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
Improves the quality of images, especially screenshots, by enhancing resolution, sharpness, and clarity. Perfect for preparing images for presentations, documentation, or social media posts.
Downloads videos from YouTube and other platforms for offline viewing, editing, or archival. Handles various formats and quality options.
Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.
Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows.
Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.
This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.
Take jaechang-hits/nejm-figure-guide from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.