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medical-imaging-review

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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

SourceBest ForStrengths
ArXivLatest methods, deep learning advancesPreprints, fast access, CS/AI focus
PubMedClinical validation, medical contextPeer-reviewed, MeSH indexing, clinical
ZoteroOrganized collections, existing libraryLocal 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

Source

git clone https://github.com/Ronitnair/research-skills/blob/main/medical-imaging-review/SKILL.mdView on GitHub

Overview

Guides researchers to write survey papers, systematic reviews, and literature analyses in medical imaging AI. It prescribes a structured workflow, standard templates, and strict citation practices to ensure rigor. It covers segmentation, detection, and classification across CT, MRI, and X-ray imaging.

How This Skill Works

Start by initializing a project with CLAUDE.md, IMPLEMENTATION_PLAN.md, and manuscript_draft.md. Follow the 7-phase WORKFLOW and use standard templates to organize content. Enforce hedging language, cite every claim, include required elements (Key Points, comparison tables, performance metrics, figure captions), and structure references by topic.

When to Use It

  • Writing a review paper on a specific medical imaging task (e.g., segmentation in CT).
  • Creating a survey or systematic review of deep learning methods for MRI or X-ray imaging.
  • Performing a literature analysis that compares methods across datasets and metrics.
  • Preparing a manuscript with a structured evaluation framework and figure placeholders.
  • Compiling references and organizing them by topic for an 80-120 entry review.

Quick Start

  1. Step 1: Initialize project with CLAUDE.md, IMPLEMENTATION_PLAN.md, and Manuscript_draft.md.
  2. Step 2: Follow the 7-phase WORKFLOW.md and use the standard templates in TEMPLATES.md.
  3. Step 3: Populate with Key Points, tables, figures, and topic-organized references; apply hedging and thorough citations.

Best Practices

  • Use hedging language (may, suggests, appears to) and avoid absolute claims.
  • Cite every claim with evidence and ensure each method section includes a Limitations paragraph.
  • Include a Key Points box (3-5 bullets) after the title and a comparison table for major sections.
  • Present performance metrics with a consistent format (e.g., Dice: 0.XXX, HD95: X.XX mm).
  • Organize references by topic and use standard templates to maintain consistency.

Example Use Cases

  • Systematic review of deep learning for lung nodule segmentation in CT.
  • Survey of MRI tumor classification methods using transformer-based architectures.
  • Literature analysis comparing X-ray fracture detection algorithms across datasets.
  • Review paper on medical imaging transformers in radiology.
  • 综述: deep learning for medical imaging review with MeSH-guided searches and citation structure.

Frequently Asked Questions

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