Write comprehensive literature reviews for medical imaging AI research. Use when writing survey papers, systematic reviews, or literature analyses on topics like segmentation, detection, classification in CT, MRI, X-ray imaging. Triggers on requests for "review paper", "survey", "literature review", "综述", or mentions of writing academic reviews on deep learning for medical imaging.
Installation
Details
Usage
After installing, this skill will be available to your AI coding assistant.
Verify installation:
npx agent-skills-cli listSkill Instructions
name: medical-imaging-review description: Write comprehensive literature reviews for medical imaging AI research. Use when writing survey papers, systematic reviews, or literature analyses on topics like segmentation, detection, classification in CT, MRI, X-ray imaging. Triggers on requests for "review paper", "survey", "literature review", "综述", or mentions of writing academic reviews on deep learning for medical imaging. allowed-tools:
- Read
- Write
- Edit
- Glob
- Grep
- Bash
- WebSearch
- WebFetch
- TodoWrite
Medical Imaging AI Literature Review Writing Skill
A systematic workflow for writing comprehensive literature reviews in medical imaging AI.
Quick Start
When user requests a literature review:
-
Initialize project with three core files:
CLAUDE.md- Writing guidelines and terminologyIMPLEMENTATION_PLAN.md- Staged execution planmanuscript_draft.md- Main manuscript
-
Follow the 7-phase workflow (see WORKFLOW.md)
-
Use standard templates (see TEMPLATES.md)
Core Principles
Writing Style
- Use hedging language: "may", "suggests", "appears to", "has shown promising results"
- Avoid absolute claims: Never say "X is the best method"
- Every claim needs citation support
- Each method section needs a Limitations paragraph
Required Elements
- Key Points box (3-5 bullets) after title
- Comparison table for each major section
- Performance metrics with consistent format (Dice: 0.XXX, HD95: X.XX mm)
- Figure placeholders with detailed captions
- References organized by topic (80-120 typical)
Paragraph Structure
- Topic sentence (main claim)
- Supporting evidence (citations + data)
- Analysis (critical evaluation)
- Transition to next paragraph
Literature Sources
ArXiv MCP (Preprints & Latest Research)
GitHub: https://github.com/blazickjp/arxiv-mcp-server
Available Tools:
search_papers- Search by keywords with date range and category filtersdownload_paper- Download paper by arXiv IDlist_papers- List all downloaded papersread_paper- Read downloaded paper content
Configuration:
{
"mcpServers": {
"arxiv": {
"command": "uvx",
"args": ["arxiv-mcp-server"],
"env": {
"ARXIV_STORAGE_PATH": "~/.arxiv-mcp-server/papers"
}
}
}
}
Usage Example:
Search: "medical image segmentation transformer"
Categories: cs.CV, eess.IV
Date range: 2023-01-01 to present
Max results: 50
PubMed MCP (Biomedical Literature)
GitHub: https://github.com/grll/pubmedmcp
Access 35+ million biomedical literature citations.
Configuration:
{
"mcpServers": {
"pubmedmcp": {
"command": "uvx",
"args": ["pubmedmcp@latest"],
"env": {
"UV_PRERELEASE": "allow",
"UV_PYTHON": "3.12"
}
}
}
}
Search Tips:
- Use MeSH terms for precise medical searches
- Combine with publication type filters (Review, Clinical Trial)
- Filter by date for recent literature
Zotero Integration (Reference Management)
Access local Zotero database (Requires the user to provide their user ID.):
# List collections
curl -s "http://localhost:23119/api/users/[USER_ID]/collections"
# Get items from collection
curl -s "http://localhost:23119/api/users/[USER_ID]/collections/[KEY]/items"
Alternatively, Zotero-MCP can be used, but it requires users to perform manual configuration in advance.
Extract: title, abstractNote, date, creators, publicationTitle, DOI
Source Selection Guide
| Source | Best For | Strengths |
|---|---|---|
| ArXiv | Latest methods, deep learning advances | Preprints, fast access, CS/AI focus |
| PubMed | Clinical validation, medical context | Peer-reviewed, MeSH indexing, clinical |
| Zotero | Organized collections, existing library | Local management, annotations, PDFs |
Standard Review Structure
# [Title]: State of the Art and Future Directions
## Key Points
- [3-5 bullets summarizing main findings]
## Abstract
## 1. Introduction
### 1.1 Clinical Background
### 1.2 Technical Challenges
### 1.3 Scope and Contributions
## 2. Datasets and Evaluation Metrics
### 2.1 Public Datasets
**Table 1. Public Datasets**
| Dataset | Year | Cases | Annotation | Access |
### 2.2 Evaluation Metrics
## 3. Deep Learning Methods
### 3.1 [Category 1]
### 3.2 [Category 2]
...
**Table 2. Method Comparison**
| Reference | Category | Architecture | Dataset | Performance | Innovation |
## 4. Downstream Applications
## 5. Commercial Products & Clinical Translation
**Table 3. Commercial Products**
## 6. Discussion
### 6.1 Current Limitations
### 6.2 Future Directions
## 7. Conclusion
## References
Method Description Template
### 3.X [Method Category]
[1-2 paragraph introduction with motivation]
**[Method Name]:** [Author] et al. [ref] proposed [method], which [innovation]:
- [Key component 1]
- [Key component 2]
Achieves Dice of X.XX on [dataset].
**Mathematical Formulation:** (if applicable)
$$\mathcal{L} = \mathcal{L}_{seg} + \lambda \mathcal{L}_{aux}$$
**Limitations:** Despite advantages, [category] methods face: (1) [limit 1]; (2) [limit 2].
Citation Patterns
# Data citation
"...achieved Dice of 0.89 [23]"
# Method citation
"Gu et al. [45] proposed..."
# Multi-citation
"Several studies demonstrated... [12, 15, 23]"
# Comparative
"While [12] focused on..., [15] addressed..."
Quality Checklist
Before completion, verify:
- Key Points present (3-5 bullets)
- Table per major section
- Limitations for each method category
- Consistent terminology throughout
- Hedging language used appropriately
- 80-120 references, organized by topic
- Figure placeholders with captions
File References
- WORKFLOW.md - Detailed 7-phase workflow
- TEMPLATES.md - CLAUDE.md and IMPLEMENTATION_PLAN.md templates
- DOMAINS.md - Domain-specific method categories
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