home / skills / luwill / research-skills / medical-imaging-review

medical-imaging-review skill

/medical-imaging-review

This skill generates comprehensive medical imaging literature reviews following a structured seven-phase workflow, ensuring systematic citations, method

npx playbooks add skill luwill/research-skills --skill medical-imaging-review

Review the files below or copy the command above to add this skill to your agents.

Files (6)
SKILL.md
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---
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,
  ultrasound, or pathology imaging. Triggers on requests for "review paper",
  "survey", "literature review", "综述", "systematic review", or mentions of
  writing academic reviews on deep learning for medical imaging.
metadata:
  author: user
  version: "2.0.0"
allowed-tools:
  - Read
  - Write
  - Edit
  - Glob
  - Grep
  - Bash
  - WebSearch
  - WebFetch
  - Task
  - mcp__arxiv-mcp-server__search_papers
  - mcp__arxiv-mcp-server__download_paper
  - mcp__arxiv-mcp-server__read_paper
  - mcp__pubmed-mcp-server__pubmed_search_articles
  - mcp__zotero__zotero_search_items
  - mcp__zotero__zotero_get_item_fulltext
---

# Medical Imaging AI Literature Review Skill

Write comprehensive literature reviews following a systematic 7-phase workflow.

## Quick Start

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 [references/WORKFLOW.md](references/WORKFLOW.md))

3. **Use domain-specific templates** (see [references/DOMAINS.md](references/DOMAINS.md))

---

## Core Principles

### Writing Style
- **Hedging language**: "may", "suggests", "appears to", "has shown promising results"
- **Avoid absolutes**: Never say "X is the best method"
- **Citation support**: Every claim needs reference
- **Limitations**: 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**: Dice (0.XXX), HD95 (X.XX mm)
- **Figure placeholders** with detailed captions
- **References**: 80-120 typical, organized by topic

### Paragraph Structure
```
Topic sentence (main claim)
  → Supporting evidence (citations + data)
  → Analysis (critical evaluation)
  → Transition to next paragraph
```

---

## Literature Sources

Use multi-source strategy for comprehensive coverage:

| Source | Best For | Tools |
|--------|----------|-------|
| ArXiv | Latest DL methods, preprints | `search_papers`, `read_paper` |
| PubMed | Clinical validation, peer-reviewed | `pubmed_search_articles` |
| Zotero | Existing library, organized refs | `zotero_search_items` |

For MCP configuration details, see [references/MCP_SETUP.md](references/MCP_SETUP.md).

---

## Standard Review Structure

```markdown
# [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)
### 2.2 Evaluation Metrics

## 3. Deep Learning Methods
### 3.1 [Category 1]
### 3.2 [Category 2]
(Table 2: Method Comparison)

## 4. Downstream Applications

## 5. Commercial Products & Clinical Translation (Table 3)

## 6. Discussion
### 6.1 Current Limitations
### 6.2 Future Directions

## 7. Conclusion

## References
```

---

## Method Description Template

```markdown
### 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].

**Limitations:** Despite advantages, [category] methods face:
(1) [limit 1]; (2) [limit 2].
```

---

## Citation Patterns

```markdown
# 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..."
```

---

## Reference Files

| File | Purpose |
|------|---------|
| [references/WORKFLOW.md](references/WORKFLOW.md) | Detailed 7-phase workflow |
| [references/TEMPLATES.md](references/TEMPLATES.md) | CLAUDE.md and IMPLEMENTATION_PLAN.md templates |
| [references/DOMAINS.md](references/DOMAINS.md) | Domain-specific method categories |
| [references/MCP_SETUP.md](references/MCP_SETUP.md) | MCP server configuration |
| [references/QUALITY_CHECKLIST.md](references/QUALITY_CHECKLIST.md) | Pre-submission quality checklist |

Overview

This skill produces comprehensive literature reviews for medical imaging AI research, following a systematic 7-phase workflow and domain-specific templates. It is designed to create structured survey papers, systematic reviews, and critical literature analyses across modalities such as CT, MRI, X-ray, ultrasound, and pathology imaging.

How this skill works

The skill guides you through staged execution: project initialization, targeted literature search across sources (preprints and peer-reviewed), structured method synthesis, quantitative comparisons, and manuscript drafting with required elements (key points, comparison tables, metrics, and figure placeholders). It enforces hedging language, citation support for claims, and explicit limitations for every method section.

When to use it

  • Writing a survey or state-of-the-art paper on segmentation, detection, or classification in medical imaging.
  • Preparing a systematic review or meta-analysis that requires standardized evaluation metrics and dataset summaries.
  • Creating a critical methods comparison for a grant, thesis chapter, or conference tutorial.
  • Mapping clinical translation and regulatory readiness of imaging AI products.
  • Generating manuscript-ready drafts with figures, tables, and organized references for submission.

Best practices

  • Adopt hedging language and avoid absolute statements; support each claim with citations.
  • Include a 3–5 bullet Key Points summary and a limitations paragraph for every method category.
  • Use standardized performance metrics (e.g., Dice, HD95) and report values with dataset context.
  • Provide comparison tables per major section and clear figure placeholders with descriptive captions.
  • Search multiple sources (arXiv for recent methods, PubMed for clinical validation) and collate references centrally.

Example use cases

  • A systematic review comparing deep learning segmentation methods for brain MRI with Dice/HD95 meta-analysis.
  • A survey paper summarizing detection and classification advances in chest X-ray AI, including public dataset table.
  • A literature analysis highlighting clinical validation and regulatory steps for AI products in pathology imaging.
  • A methods-focused chapter that organizes architectures, training strategies, and failure modes across ultrasound tasks.

FAQ

How many references should a comprehensive review include?

Typical comprehensive reviews include roughly 80–120 references, organized by topic and use case.

Which data sources are recommended for balanced coverage?

Combine arXiv for recent preprints, PubMed for peer-reviewed clinical studies, and a reference manager (e.g., Zotero) to organize citations.