Clinical-Trial-Inspector
🥈SilverThe Clinical Trial Inspector is a cutting-edge AI tool that helps researchers and clinicians to derive actionable insights from clinical trial data. Utilizing advanced techniques like Semantic Search and Visual Analytics, it enhances data exploration and decision-making.
700Updated 2mo ago
Advanced30 minutes to implementresearch
Saves ~210 min per use
Quick InstallView Source
claude install geoffkip/Clinical-Trial-InspectorWorks with:
ChatGPTGitHub Copilot
Overview
About This Skill
Clinical Trial Inspector is an AI agent that helps researchers, clinicians, and analysts explore clinical trial data through semantic search, retrieval-augmented generation, and interactive visualizations. It combines hybrid search (semantic + keyword matching) with multi-filter analytics to discover relevant trials and identify trends across 60,000+ studies. The tool automatically generates inline charts, knowledge graphs, and geospatial maps to reveal connections between studies, sponsors, and medical conditions. Built on LangChain, LlamaIndex, and Google Gemini, it handles complex queries like trial comparisons and translates eligibility criteria into SQL for patient cohort identification.
How to Use
Sign up for a free Gemini API key at aistudio.google.com/app/api-keys. Paste your key into the Streamlit app deployed on Hugging Face Spaces. Ask natural language questions about trials—the agent automatically selects the right tool (search, analytics, comparison, or cohort SQL generation) based on your query.
Use Cases
Extracting insights from clinical trial data
Visualizing trial results for presentations
Conducting semantic searches for specific trial criteria
Generating reports on clinical trial trends
Best For
ResearchGrowth
Tags
Setup & Installation
Quick Install
Terminal
claude install geoffkip/Clinical-Trial-InspectorAlternative Install (Git Clone)
git clone https://github.com/geoffkip/Clinical-Trial-InspectorRequirements
- Claude Code or compatible AI agent
- Works with: ChatGPT, GitHub Copilot
- Advanced skill - requires experience with AI agents
Quick Start Guide
1
Install the Skill
Copy the install command above and run it in your terminal.
2
Open Your AI Agent
Launch Claude Code, Cursor, or your preferred AI coding agent.
3
Try It Out
Use the prompt template or examples below to test the skill.
4
Customize
Adapt the skill to your specific use case and workflow.
Usage Examples
Prompt Template
Analyze the clinical trial data for [DRUG_NAME] targeting [DISEASE]. Identify key trends in patient response, adverse events, and efficacy metrics. Highlight any outliers or anomalies that warrant further investigation. Suggest potential adjustments to the trial protocol based on these findings.
Example Output
After analyzing the clinical trial data for 'Nexarib' targeting 'Metastatic Breast Cancer', several key trends emerged. The primary efficacy endpoint, Progression-Free Survival (PFS), showed a median of 10.5 months in the treatment arm versus 7.8 months in the control arm. However, a notable outlier was observed in Cohort B, where 15% of patients experienced a severe immune-related adverse event (irAE) not seen in other cohorts. This could be linked to the concurrent use of corticosteroids, as 80% of these patients were on a medium to high dose. Additionally, the response rate in patients with PD-L1 expression ≥50% was significantly higher (45%) compared to those with lower expression (22%). Based on these findings, I recommend implementing a more rigorous monitoring protocol for irAEs in Cohort B and considering a stratified analysis based on PD-L1 expression levels in future reports.
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