| 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 f... |
| license | MIT |
| author | 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
-
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 references/WORKFLOW.md)
-
Use domain-specific templates (see 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.
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
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].
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..."
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.
