Refactor high-complexity React components in Dify frontend. Use when `pnpm analyze-component...
npx skills add Ronitnair/research-skills --skill "medical-imaging-review"
Install specific skill from multi-skill repository
# 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.
# SKILL.md
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 plan-
manuscript_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 filters
- download_paper - Download paper by arXiv ID
- list_papers - List all downloaded papers
- read_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
# Supported AI Coding Agents
This skill is compatible with the SKILL.md standard and works with all major AI coding agents:
Learn more about the SKILL.md standard and how to use these skills with your preferred AI coding agent.