medical-imaging-review

name: medical-imaging-reviewdescription: 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 f...license: MITauthor: AIPOCH

Source: https://github.com/aipoch/medical-research-skills

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)

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

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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

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Literature Sources

Use multi-source strategy for comprehensive coverage:

For MCP configuration details, see references/MCP_SETUP.md.

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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)
### 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


## Input Validation

This skill accepts requests that match the documented purpose of `medical-imaging-review` and include enough context to complete the workflow safely.

Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:

> `medical-imaging-review` only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.

## References

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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].

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

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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..."

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Reference Files

Error Handling

• If required inputs are missing, state exactly which fields are missing and request only the minimum additional information.
• If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment.
• If execution fails, report the failure point, summarize what can still be completed safely, and provide a manual fallback.
• Do not fabricate files, citations, data, search results, or execution outcomes.