medical-imaging-review

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:

1. Initialize project with three core files:

   • CLAUDE.md - Writing guidelines and terminology
   • IMPLEMENTATION_PLAN.md - Staged execution plan
   • manuscript_draft.md - Main manuscript

2. Follow the 7-phase workflow (see WORKFLOW.md)

3. 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

1. Topic sentence (main claim)
2. Supporting evidence (citations + data)
3. Analysis (critical evaluation)
4. 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