Professional Medical Research Platform

Platform Overview

MedResearch AI Platform is a comprehensive medical research intelligence system designed specifically for healthcare professionals conducting serious medical research. The platform combines advanced AI capabilities with evidence-based methodologies to accelerate and enhance the quality of medical research.

Medical AI Intelligence

Advanced AI analysis of medical documents, literature synthesis, and evidence-based recommendations with clinical relevance scoring.

Collaborative Research

Real-time collaborative authoring environment with version control, citation management, and publication-ready outputs.

Key Capabilities

  • Literature analysis with AI-powered insights and evidence quality assessment
  • Medical document processing with clinical significance evaluation
  • Proactive research suggestions based on document context and specialty
  • Case study generation from multiple medical files with publication potential
  • Professional research authoring with citation management and collaboration

Getting Started-Contact

Step 1: Platform Access

Navigate to the platform and complete the medical professional verification process. Set up your research profile with relevant specialties and institutional affiliations.

Platform URLs:
• Landing Page: https://medresearch-ai.org/
• Research Dashboard:https://medresearch-ai.org/authoring-dashboard
• Contact : admin@medresearch-ai.org

Step 2: Interface Familiarization

Explore the two main interface areas: the Landing Page for research discovery and quick access, and the Authoring Dashboard for comprehensive research workflows.

Step 3: AI Assistant Setup

Configure the AI Research Assistant in the sidebar with your medical specialty and research preferences. The AI will build context as you work with documents and content.

Core Research Workflows

Literature Review Workflow

  1. Formulate research question using natural language in AI Assistant
  2. Select appropriate medical specialty and analysis type
  3. Review AI-generated insights with confidence scores and evidence quality
  4. Integrate findings into research document with proper citations
  5. Generate follow-up questions for deeper investigation

Medical Document Analysis

  1. Upload medical documents (PDFs, images, clinical reports)
  2. Wait for OCR processing and content extraction
  3. Initiate AI analysis for comprehensive medical intelligence
  4. Review analysis sections: summary, findings, recommendations
  5. Extract specific data points and integrate into research

Collaborative Research

  1. Create research project with appropriate permissions
  2. Invite collaborators and establish research protocols
  3. Use real-time editor with AI suggestions and version control
  4. Manage citations and references collectively
  5. Generate publication-ready exports with validation
🤖 Advanced AI Co-Pilot System

AI Co-Pilot: Intelligent Writing Assistant

The AI Co-Pilot represents a revolutionary approach to medical writing assistance, featuring three sophisticated phases that transform how researchers create, enhance, and collaborate on medical documents.

Phase 1: Full Context Awareness

Advanced document analysis with complete context understanding for intelligent content generation.

🎯 Smart Continue Writing

  • Full Document Analysis: Understands entire document context
  • Intelligent Positioning: Continues from exact cursor position
  • Style Consistency: Maintains academic tone and terminology
  • Citation Integration: Includes relevant medical references

⚡ Enhanced Features

  • Specialty Awareness: Adapts to medical specialties
  • Research Methodology: Follows established approaches
  • Evidence-Based: Generates 200-500 word continuations
  • Auto-Save: Seamless document preservation

Phase 2: Goal-Oriented Actions

Right-click context menu with specialized AI actions for targeted content enhancement.

Continue Writing

AI continues from cursor position with full context awareness

📝

Elaborate

Expand selected text with additional details and context

📋

Summarize

Create concise summaries of selected content

🌐

Web Search

Find and integrate latest medical literature

Phase 3: Proactive AI Suggestions

Real-time ghost text suggestions that appear automatically as you write.

🎭 Ghost Text Technology

Smart Triggers
  • • Automatic detection when you pause typing
  • • Context-aware suggestion generation
  • • Debounced for optimal performance
  • • Medical terminology understanding
User Controls
  • Tab to accept suggestion
  • Esc to dismiss
  • • Floating suggestion display
  • • Non-intrusive ghost text styling

🔧 Technical Implementation

Backend Integration
  • • DeepSeek AI service integration
  • • FastAPI async endpoints
  • • MongoDB document storage
  • • Real-time WebSocket updates
Frontend Features
  • • CodeMirror editor integration
  • • Floating div positioning
  • • Debounced event handling
  • • Progressive loading indicators
AI Capabilities
  • • Context-aware text generation
  • • Medical literature integration
  • • Specialty-specific responses
  • • Citation and reference support
Advanced Literature Intelligence

Visual PubMed Search

The Visual PubMed Search transforms traditional literature research into an interactive, AI-powered knowledge discovery experience. Visualize research landscapes, identify gaps, and discover hidden connections across medical literature.

Knowledge Graph Visualization

Interactive 3D visualization of research connections, showing relationships between papers, topics, and research gaps in real-time.

Research Gap Detection

AI automatically identifies promising unexplored areas and cross-disciplinary opportunities in medical research.

Key Features

Smart Search Interface
  • • Natural language query processing
  • • Voice search with medical terminology
  • • Smart research templates
  • • AI-powered query suggestions
  • • Quick research templates
Analysis Capabilities
  • • Research gap identification
  • • Cross-discipline connections
  • • Research impact prediction
  • • Timeline generation
  • • Evidence synthesis

How to Use Visual PubMed Search

  1. Navigate to Visual PubMed Search from the main dashboard
  2. Enter your research question in natural language (e.g., "Latest CRISPR treatments for heart disease")
  3. Select analysis depth: Quick Insights, Deep Analysis, or Comprehensive Research
  4. Choose number of papers to analyze (20-75 papers)
  5. Click "Analyze Research Landscape" to start AI processing
  6. Explore the interactive knowledge graph visualization
  7. Review identified research gaps and opportunities
  8. Export findings to your research workspace

Research Templates

Find Research Gaps

Identify unexplored areas

Compare Treatments

Analyze treatment approaches

Clinical Trials

Latest trial results

Evidence Synthesis

Comprehensive reviews

Pro Tips for Visual PubMed Search

  • Use specific medical terminology for more precise results
  • Try voice search for complex queries - it understands medical pronunciation
  • Use "Deep Analysis" mode for comprehensive research projects
  • Explore the knowledge graph by clicking on nodes to discover connections
  • Export research gaps to the Autonomous Research Agent for automatic investigation
Breakthrough-Level Hypothesis Generation

MedDiscovery 3.0 LITE

MedDiscovery 3.0 LITE is an advanced 9-agent AI system designed to generate breakthrough medical hypotheses through evidence-first reasoning. Unlike traditional research tools, it analyzes 500+ papers before proposing theories, ensuring all suggestions are grounded in solid scientific evidence. The system provides transparent reasoning with honest feasibility assessments.

9-Agent Pipeline

Specialized agents working in concert: Exploration, Synthesis, Validation, Integration, Innovation, Reality Check, Documentation, Confidence, and Review.

5D Evidence Scoring

Comprehensive evaluation across Novelty, Feasibility, Impact, Evidence, and Risk dimensions with transparent scoring methodology.

Domain Intelligence

Smart domain/endpoint gating system that filters relevant papers and maintains clinical context throughout analysis.

Deep Analysis

Processes 2-6 minutes per query, conducting 8-stage reasoning with systematic evidence analysis and transparent logic chains.

How It Works

1

Enter Research Query

Navigate to the Discovery page and enter your medical research question. Be specific about the disease, intervention, or mechanism you're investigating.

2

Evidence Gathering (Stage 1-2)

The Exploration Agent searches PubMed and retrieves 500+ relevant papers. Domain gates activate to filter papers by medical specialty and research endpoints.

3

Pattern Recognition (Stage 3-4)

Synthesis and Validation Agents analyze the evidence corpus to identify patterns, gaps, and unexpected connections across different research domains.

4

Hypothesis Generation (Stage 5-6)

Integration and Innovation Agents propose novel hypotheses grounded in the evidence, with Reality Check Agent assessing practical feasibility.

5

Scoring & Documentation (Stage 7-8)

Confidence Agent applies 5D scoring (Novelty, Feasibility, Impact, Evidence, Risk). Documentation Agent compiles results with transparent reasoning chains.

6

Review & Export

Final Review Agent validates all claims. Results include hypotheses ranked by potential, supporting evidence citations, implementation roadmaps, and risk assessments.

Pro Tips

  • Frame queries as specific clinical problems or mechanistic questions for best results
  • Allow full processing time (2-6 minutes) - rushing interrupts the evidence analysis
  • Review the reasoning chains to understand how hypotheses were derived from evidence
  • Pay attention to the Risk scores - they indicate practical implementation challenges
  • Export results to Research Studio to develop hypotheses into full research protocols
AI-Powered Research Automation

Autonomous Research Agent

The Autonomous Research Agent represents the pinnacle of AI-powered medical research automation. Simply provide a research topic, and the agent conducts complete end-to-end research, from literature search to publication-ready paper generation.

Fully Autonomous Operation

Complete research automation from topic analysis to final paper generation, requiring minimal human intervention.

Extended Research Capability

Supports complex research projects with up to 2-hour processing time for comprehensive literature analysis and synthesis.

Research Phases

Research Planning

AI analyzes topic and creates comprehensive research strategy

Literature Search

Multi-database search with smart paper filtering

Content Analysis

Deep analysis of selected papers and evidence extraction

Knowledge Synthesis

Synthesis of evidence into coherent research narrative

Paper Writing

Generation of publication-ready research paper

Quality Validation

Final validation and quality assurance

How to Use the Autonomous Research Agent

  1. Navigate to Autonomous Research Agent from the main dashboard
  2. Enter a detailed research topic (minimum 20 characters for best results)
  3. Click "Start Autonomous Research" to begin the process
  4. Monitor real-time progress through the 6-phase research pipeline
  5. Wait for completion (typically 30-120 minutes depending on complexity)
  6. Review the generated research paper and analysis
  7. Download results in multiple formats (Markdown, PDF, Word)
  8. Export to your research workspace for further editing

Advanced Features

Research Optimization
  • • Smart paper filtering (top 10 most relevant)
  • • Medical keyword extraction
  • • Recent papers prioritization
  • • 70% faster processing with cost optimization
  • • Extended timeout support (up to 2 hours)
Output Formats
  • • Publication-ready research paper
  • • Structured abstract and conclusions
  • • Comprehensive literature review
  • • Research methodology section
  • • Properly formatted citations

Best Practices for Autonomous Research

  • Be Specific: Provide detailed research topics with specific medical conditions, treatments, or populations
  • Include Context: Mention time frames, study types, or specific research questions you want answered
  • Monitor Progress: The agent provides real-time updates - use this to understand the research process
  • Review Results: Always review the generated paper for accuracy and add your clinical expertise
  • Use as Foundation: Treat the output as a comprehensive starting point for your research

Example Research Topics

Good Examples:
  • • "Efficacy of CRISPR-Cas9 gene therapy in treating sickle cell disease: systematic review of clinical trials 2020-2024"
  • • "Comparison of immunotherapy approaches for metastatic melanoma: checkpoint inhibitors vs CAR-T cell therapy"
  • • "Long-term cardiovascular outcomes of GLP-1 receptor agonists in type 2 diabetes patients"
Avoid:
  • • "Cancer treatment" (too broad)
  • • "Heart disease" (lacks specificity)
  • • "New drugs" (no clear focus)
🚀 AI-Powered Statistical Analysis Platform

Research Studio: Advanced Statistical Analysis

Research Studio is a comprehensive statistical analysis platform that combines traditional statistical methods with cutting-edge AI-powered visualization and analysis capabilities. Upload your research data and receive publication-quality statistical analysis with professional visualizations in minutes.

AI-Powered Analysis Engine

DeepSeek R1 Integration

State-of-the-art AI model generates custom Python code (matplotlib, seaborn, statsmodels) for publication-quality visualizations at 300 DPI.

Safe Execution Environment

All AI-generated code runs in isolated subprocess with 30-second timeout and restricted system access for maximum security.

9 Statistical Test Types

From basic comparisons (t-test, ANOVA) to advanced analyses (regression, survival analysis, meta-analysis, mixed models).

Export to Markdown

Complete analysis exported as Markdown with embedded base64 plots, ready for PDF/HTML conversion and publication.

The 6-Step Analysis Wizard

1 Upload Your Research Data

Import your dataset using one of two methods:

File Upload
  • • Drag & drop CSV or Excel files
  • • Supports .csv, .xlsx, .xls formats
  • • Auto-detection of data types
  • • Handles missing values (NaN) automatically
Manual Data Entry
  • • Smart form with customizable columns
  • • Add/remove rows dynamically
  • • Perfect for small datasets or pilot studies
  • • Auto-converts to CSV format
Data Preparation Tips
  • Column headers: Use clear, descriptive names (e.g., "Age_Years", "Blood_Pressure_mmHg")
  • Missing values: Leave cells empty or use "NA" - system handles them automatically
  • Numeric data: Ensure numbers are in standard format (no commas, use decimal points)
  • Categorical data: Use consistent labels (e.g., "Treatment_A" not "TreatmentA" or "Tx A")
  • Date formats: Use ISO format (YYYY-MM-DD) for best results

2 Review Your Data

Examine the first 20 rows of your dataset to verify data integrity before analysis:

Preview Features
  • Data table: Interactive table showing all columns and first 20 rows
  • Column types: Auto-detected as numeric, categorical, or text
  • Summary stats: Quick overview of total rows and columns
  • Missing values: Displayed as "N/A" for transparency

Quality Check: This step is crucial! Verify that column headers are correct, data types are appropriate, and there are no obvious data entry errors before proceeding.

3 Choose Your Statistical Test

Select the appropriate test based on your research question. Tests are organized into Basic (traditional methods) and Advanced (AI-powered) categories.

Basic Statistical Tests
Compare Two Groups

Use when: Comparing means between two groups

Examples:

  • • Treatment vs Control
  • • Before vs After intervention
  • • Male vs Female outcomes

Statistical method: Independent t-test, Cohen's d effect size

Compare Multiple Groups

Use when: Comparing means across 3+ groups

Examples:

  • • Drug A vs Drug B vs Placebo
  • • Age groups (Young/Middle/Old)
  • • Disease severity levels

Statistical method: One-way ANOVA, Tukey HSD post-hoc

Find Relationships

Use when: Exploring correlations between variables

Examples:

  • • Age vs Blood Pressure
  • • BMI vs Cholesterol
  • • Dose vs Response

Statistical method: Pearson correlation, scatter plots

Describe My Data

Use when: Generating summary statistics

Examples:

  • • Baseline characteristics table
  • • Demographic summaries
  • • Exploratory data analysis

Statistical method: Mean, SD, ranges, frequencies

Advanced Statistical Tests (AI-Powered)

AI REQUIRED These tests automatically enable the AI Agent and generate custom analysis code

Regression Analysis
AI

Capabilities:

  • • Linear & logistic regression
  • • R², adjusted R², coefficients with p-values
  • • Residual plots & diagnostic tests
  • • Feature importance visualization

Libraries: statsmodels, scikit-learn

Survival Analysis
AI

Capabilities:

  • • Kaplan-Meier survival curves
  • • Log-rank test, hazard ratios
  • • Risk tables & median survival
  • • Censoring visualization

Libraries: lifelines

Meta-Analysis
AI

Capabilities:

  • • Forest plots with pooled effects
  • • Fixed & random effects models
  • • I² heterogeneity statistics
  • • Funnel plots for publication bias

Libraries: meta-analysis, scipy

Power Analysis
AI

Capabilities:

  • • Sample size calculations
  • • Power curves & effect sizes
  • • Type I/II error probabilities
  • • Multiple significance levels

Libraries: statsmodels.stats.power

Mixed Models
AI

Capabilities:

  • • Repeated measures ANOVA
  • • Random & fixed effects
  • • ICC (Intraclass Correlation)
  • • Spaghetti plots for trajectories

Libraries: statsmodels MixedLM

AI-Powered Visualization Agent

When ENABLED (default for advanced tests):

  • • AI generates custom Python code (matplotlib, seaborn)
  • • Publication-quality plots at 300 DPI
  • • Plain English interpretation of results
  • • Takes 15-30 seconds for complex analyses

When DISABLED (basic tests only):

  • • Uses pre-built chart.js visualizations
  • • Faster results (instant)
  • • Standard statistical plots
  • • Limited to basic test types

Auto-Enable Feature: When you select an advanced test, the AI Agent automatically enables itself (mandatory for these analyses).

4 Run Analysis

Click the magic button to execute your analysis. The system will:

Analysis Pipeline
1

Data Validation

Checks data types, handles missing values, validates structure

2

Statistical Computation

Runs appropriate statistical tests, calculates p-values, effect sizes

3

AI Code Generation (if enabled)

DeepSeek R1 generates custom visualization code based on your data

4

Safe Code Execution

Runs code in isolated subprocess with timeout protection

5

Plot Capture & Interpretation

Saves plots as 300 DPI PNG, converts to base64, generates explanation

Expected Processing Time
  • Basic tests (AI disabled): Instant (< 1 second)
  • Basic tests (AI enabled): 15-20 seconds
  • Advanced tests: 20-30 seconds
  • Large datasets (>1000 rows): Add 5-10 seconds

5 Review Results

Comprehensive results display with multiple sections:

Plain English Interpretation

Non-technical summary of findings written for medical professionals. Explains what the statistics mean in clinical context.

AI-Generated Visualizations

Publication-quality plots at 300 DPI with:

  • • Professional styling (seaborn themes)
  • • Medical/scientific color schemes
  • • Clear labels, legends, and annotations
  • • Statistical annotations (p-values, confidence intervals)
  • • Download buttons for each plot (PNG format)
Statistical Output

Detailed statistical results including test statistics, p-values, effect sizes, confidence intervals, and diagnostic information.

Methods & Results Text

Auto-generated text for your manuscript's Methods and Results sections, following APA/AMA style guidelines.

Generated Code (Advanced Tests)

View the actual Python code generated by AI for transparency and reproducibility. Can be copied and run independently.

6 Export Your Analysis

Save your complete analysis in multiple formats:

Markdown (.md)
  • • Embedded base64 plots
  • • All statistical output
  • • Methods & Results text
  • • AI interpretation
  • • Ready for GitHub/GitLab
PDF Preview
  • • Professional formatting
  • • High-resolution plots
  • • Print-ready output
  • • Perfect for sharing
  • • One-click generation
HTML Preview
  • • Interactive viewing
  • • Styled with CSS
  • • Easy sharing via link
  • • Responsive design
  • • Browser-compatible
What Gets Exported
  • Analysis metadata: Date, test type, data summary
  • Plain English interpretation: Your main findings
  • Statistical results: All numbers, p-values, effect sizes
  • Visualizations: Embedded as base64 images (no external files needed)
  • Methods section: Copy-paste ready text for your manuscript
  • Results section: APA/AMA style reporting
  • AI interpretation: Detailed explanation of plots and findings
  • Generated code: Full Python code for reproducibility (advanced tests)

Technical Architecture

Backend Components

  • FastAPI: High-performance async REST API
  • MongoDB: Document storage for projects and data
  • Pandas: Data manipulation and analysis
  • SciPy/NumPy: Statistical computations
  • DeepSeek R1 API: AI code generation
  • Subprocess isolation: Safe code execution

AI Agent Libraries

  • matplotlib: Core plotting library (300 DPI)
  • seaborn: Statistical visualizations
  • statsmodels: Advanced statistical models
  • scikit-learn: Machine learning algorithms
  • lifelines: Survival analysis
  • scipy.stats: Statistical tests

Security Measures

  • • All AI-generated code runs in isolated subprocess (no system access)
  • • 30-second timeout prevents infinite loops
  • • Temporary directories cleaned after execution
  • • No file system writes outside designated folders
  • • Input validation and sanitization for all uploads
  • • Authentication required for all API endpoints

Best Practices for Research Studio

Data Preparation

  • • Clean your data before upload (check for typos, inconsistencies)
  • • Use meaningful column names that describe the variable
  • • For categorical variables, use consistent labels across all rows
  • • Document your data collection methods for reproducibility
  • • Keep a backup of your original raw data

Test Selection

  • • Choose tests BEFORE looking at data (avoid p-hacking)
  • • Consider your research question and study design
  • • Check statistical assumptions (normality, independence)
  • • Use advanced tests only when methodologically appropriate
  • • Consult a statistician for complex analyses

Interpretation

  • • Statistical significance (p < 0.05) ≠ clinical significance
  • • Always report effect sizes, not just p-values
  • • Consider confidence intervals for practical importance
  • • Discuss limitations and potential confounders
  • • Don't over-interpret exploratory analyses

Reproducibility

  • • Save the exported Markdown file with your project
  • • Document AI Agent settings and versions
  • • Keep the generated Python code for transparency
  • • Include data preprocessing steps in your methods
  • • Share analysis parameters in supplementary materials

Troubleshooting Common Issues

❌ "Test type not supported"

Cause: Backend not recognizing test type

Solution: Refresh page, clear browser cache, or contact admin if persists

❌ CSV upload fails with 500 error

Cause: Data formatting issues or special characters

Solution: Check for unusual characters, save as UTF-8, remove empty rows/columns

⏱️ Analysis takes too long (>60 seconds)

Cause: Large dataset or complex AI generation

Solution: Reduce dataset size, disable AI Agent for quick tests, or try simpler test type

🖼️ Plots not displaying

Cause: Browser blocking base64 images or slow network

Solution: Check browser console for errors, disable ad blockers, refresh page

📁 Export download fails

Cause: Browser download restrictions or large file size

Solution: Allow pop-ups/downloads from site, check storage space, try different browser

Quick Reference Guide

File Formats Supported

  • ✅ .csv (Comma-separated values)
  • ✅ .xlsx (Excel 2007+)
  • ✅ .xls (Excel 97-2003)
  • ❌ .sav, .dta, .json (not yet supported)

Data Limits

  • Max rows: 10,000 (larger requires server config)
  • Max columns: 100
  • Max file size: 10MB
  • Manual entry: 50 rows recommended

Keyboard Shortcuts

  • Ctrl + Enter → Run Analysis
  • Ctrl + S → Export Markdown
  • Ctrl + P → Preview PDF
  • Esc → Close modals

Browser Requirements

  • ✅ Chrome 90+ (recommended)
  • ✅ Firefox 88+
  • ✅ Edge 90+
  • ⚠️ Safari 14+ (limited support)

Publication Hub

Comprehensive Publication Management

The Publication Hub provides a centralized platform for managing all aspects of research publication, from ORCID integration to one-click Zenodo publishing with perfect format preservation.

ORCID Professional Profile

Connect your research to your professional ORCID profile for academic recognition and proper attribution.

Features:
  • Profile Connection: Link your ORCID ID to all publications
  • Automatic Attribution: Research automatically credited to your profile
  • Academic Recognition: Enhance discoverability and citation tracking
  • Professional Networking: Connect with global research community

Export Ready Files

Download your research in multiple scientific formats optimized for publication and sharing.

HTML Export

Perfect format preservation with charts, equations, and interactive elements.

LaTeX Export

Academic-grade formatting for journal submissions and conferences.

Export Management
  • Version Control: Track and manage multiple export versions
  • Format Optimization: Automatic formatting for target platforms
  • Preview System: Review exports before publication
  • Batch Processing: Export multiple documents simultaneously

Zenodo One-Click Publication

Publish your research directly to Zenodo with automatic DOI assignment and perfect format preservation.

Zenodo Integration Features
Publication Process
  • • Immediate DOI assignment
  • • Automatic community submission
  • • Multilingual support (English & Greek)
  • • Professional metadata handling
Format Preservation
  • • 100% HTML formatting preservation
  • • Chart and visualization integrity
  • • MathJax equation rendering
  • • Table structure maintenance
Publication Workflow
  1. Complete your research in the authoring workspace
  2. Click "Publication Hub" to access publication tools
  3. Select "Publish to Zenodo" for one-click publication
  4. Fill in publication metadata (title, authors, abstract, keywords)
  5. Choose publication type and language settings
  6. Click "Publish to Zenodo" to complete publication
  7. Receive immediate DOI and publication URL
  8. Research automatically submitted to MedResearch AI community

Academic Repository Integration

Direct links and integration with major academic repositories and platforms.

Zenodo

Open Science Repository

Figshare

Research Data Repository

ResearchGate

Academic Network

Peer-Reviewed Journal Access

Quick access to submission portals for major medical and scientific journals.

Featured Journals
• Nature
• The Lancet
• BMJ
• PLOS ONE
• Frontiers
• Cell
• Science
• NEJM

Publication Best Practices

Professional Tips
  • Connect ORCID first for professional recognition
  • Use HTML exports for perfect formatting preservation
  • Check journal guidelines before submission
  • Leverage Zenodo DOI for immediate citable references
  • Submit to peer-reviewed journals for academic credibility
  • Utilize community features for collaboration and feedback

Public Research Library

Share Your Research with the Medical Community

The Public Research Library allows you to share your completed research projects with the global medical community, making your work discoverable and accessible to other healthcare professionals.

📋 Publishing Workflow

⚠️ Important: Save Before Publishing

You must save your project first before publishing to the Public Library. The publishing process captures the saved content, not the current editor content.

1
Create & Edit Content: Add your research content, charts, tables, and analysis in the authoring workspace.
2
Save Project: Click the "Save Project" button to save your content to the database.
3
Publish to Library: Click "Publish to Public Library" to make your research publicly accessible.

🎯 Content Requirements

Required Elements:
  • Meaningful title (not "Untitled Project")
  • Real research content (not template text)
  • Proper abstract/description
  • Author information
  • Medical specialty classification
Enhanced Features:
  • Charts and tables are preserved
  • Mathematical formulas (MathJax support)
  • Professional formatting maintained
  • Responsive design for all devices
  • Search and filtering capabilities

🔍 Accessing the Public Library

Browse Published Research

Visit /public-library to browse all published research projects from the medical community.

View Research Details

Click on any project to view the full research content with preserved formatting, charts, and tables.

Filter by Specialty

Use specialty filters to find research relevant to your medical field of interest.

💡 Pro Tips
  • • Always preview your content before publishing to ensure proper formatting
  • • Use descriptive titles and abstracts to make your research discoverable
  • • Include relevant keywords for better search visibility
  • • Update your published research by republishing with new content

Medical Intelligence System

Document Processing Capabilities

Supported File Types
  • • PDF documents (clinical reports, research papers)
  • • Medical images with OCR text extraction
  • • Laboratory reports and test results
  • • Continuous glucose monitoring (CGM) data
  • • Clinical notes and discharge summaries
Analysis Components
  • • Document classification and type detection
  • • Clinical significance assessment
  • • Structured information retrieval
  • • Evidence-based recommendation generation
  • • Quality assurance and confidence scoring

AI Analysis Output

Comprehensive medical analysis includes multiple structured sections:

Core Analysis
  • • Executive Summary
  • • Key Findings
  • • Clinical Recommendations
Quality Assessment
  • • Methodology Evaluation
  • • Data Quality Assessment
  • • Limitations Analysis
Clinical Context
  • • Medical Context
  • • Next Steps
  • • Professional Review Notes

Best Practices for Medical Research

Effective AI Utilization

  • Be specific about patient populations, conditions, and clinical parameters
  • Always review AI confidence scores and verify critical information
  • Use AI as a research accelerator, not a replacement for clinical judgment
  • Document AI assistance and limitations in research methodology

Research Quality Standards

  • Prioritize high-quality, peer-reviewed sources with appropriate methodology
  • Assess study methodology, sample sizes, and potential biases
  • Evaluate clinical relevance and applicability to target populations
  • Maintain detailed research logs and decision documentation

Professional Design System

Design Philosophy

The MedResearch AI Platform employs a clinical-grade design system that reflects the serious nature of medical research while maintaining usability and accessibility.

Color Palette
Primary Blue (#1e40af)
Success Green (#059669)
AI Purple (#7c3aed)
Critical Red (#dc2626)
Typography
Primary Font: Inter, Segoe UI, Roboto
Hierarchy: Clear size and weight progression
Readability: High contrast, optimal line spacing
Accessibility: WCAG 2.1 AA compliant