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Medical Imaging Review

CommunityPopular
brycewang-stanford
medical-imaging-review

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.

Overview

Publisherbrycewang-stanford
RepositoryAuto-Empirical-Research-Skills
Skill namemedical-imaging-review
Stars
3.8K
Forks
479
Bundled files
5
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by brycewang-stanford on GitHub. Read the source before you install it.

Installation

Install the Medical Imaging Review AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git /tmp/Auto-Empirical-Research-Skills
mkdir -p .claude/skills
cp -r /tmp/Auto-Empirical-Research-Skills/skills/02-luwill-research-skills/medical-imaging-review .claude/skills/medical-imaging-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Medical Imaging Review in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Medical Imaging Review on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Medical Imaging Review is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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)


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:

SourceBest ForTools
ArXivLatest DL methods, preprintssearch_papers, read_paper
PubMedClinical validation, peer-reviewedpubmed_search_articles
ZoteroExisting library, organized refszotero_search_items

For MCP configuration details, see 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

FilePurpose
references/WORKFLOW.mdDetailed 7-phase workflow
references/TEMPLATES.mdCLAUDE.md and IMPLEMENTATION_PLAN.md templates
references/DOMAINS.mdDomain-specific method categories
references/MCP_SETUP.mdMCP server configuration
references/QUALITY_CHECKLIST.mdPre-submission quality checklist

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Medical Imaging Review AI skill do?

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.

Why use Medical Imaging Review on TypingMind?

Because you install it once and use it with any model. Medical Imaging Review is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Medical Imaging Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/02-luwill-research-skills/medical-imaging-review. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Medical Imaging Review?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Medical Imaging Review?

As many as you like. As long as a model supports skills, you can use Medical Imaging Review with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Medical Imaging Review AI skill free?

It is published on GitHub by brycewang-stanford. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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