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.
Advanced AI analysis of medical documents, literature synthesis, and evidence-based recommendations with clinical relevance scoring.
Real-time collaborative authoring environment with version control, citation management, and publication-ready outputs.
Navigate to the platform and complete the medical professional verification process. Set up your research profile with relevant specialties and institutional affiliations.
Explore the two main interface areas: the Landing Page for research discovery and quick access, and the Authoring Dashboard for comprehensive research workflows.
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.
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.
Advanced document analysis with complete context understanding for intelligent content generation.
Right-click context menu with specialized AI actions for targeted content enhancement.
AI continues from cursor position with full context awareness
Expand selected text with additional details and context
Create concise summaries of selected content
Find and integrate latest medical literature
Real-time ghost text suggestions that appear automatically as you write.
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.
Interactive 3D visualization of research connections, showing relationships between papers, topics, and research gaps in real-time.
AI automatically identifies promising unexplored areas and cross-disciplinary opportunities in medical research.
Identify unexplored areas
Analyze treatment approaches
Latest trial results
Comprehensive reviews
MedDiscovery is your hypothesis engineer: it generates testable, evidence-backed biomedical hypotheses with full traceability and a clear “why.”
MedDiscovery turns a vague clinical/biomedical question into a ranked set of specific, testable hypotheses. It scans and structures the literature, extracts entities (NER), builds knowledge graphs and causal maps, weighs evidence quality, and proposes hypotheses with rationale, conflicting findings, candidate biomarkers, and suggested next steps (in-silico / in-vitro / clinical) — all with precise citations.
Produces structured, testable hypotheses (not just ideas) with evidence tables, risk/uncertainty analysis, and a transparent chain of reasoning.
R&D teams, clinical researchers, medical affairs — for faster, lower-risk preclinical/clinical hypothesis formation.
Research tool only. Not clinical guidance. All hypotheses require independent experimental and/or clinical validation.
Specialized agents working in concert: Exploration, Synthesis, Validation, Integration, Innovation, Reality Check, Documentation, Confidence, and Review.
Advanced biomedical NER using BioBERT (d4data/biomedical-ner-all) for PhD-level entity recognition. Extracts proteins, diseases, chemicals, cells, and genes with 10% higher confidence than baseline methods.
Data-driven causal graph construction from extracted biological facts. Identifies intervention points through network centrality analysis, not AI speculation. Every claim backed by PMID citations.
Real-time validation against ClinicalTrials.gov database. Reasoning chains include UniProt molecular targets, ChEMBL compounds, and active NCT trial numbers for each intervention.
Comprehensive evaluation across Novelty, Feasibility, Impact, Evidence, and Risk dimensions with transparent scoring methodology. Honest assessments with AMBER/RED flags when appropriate.
Smart domain/endpoint gating system that filters relevant papers and maintains clinical context throughout analysis. Supports 9 medical domains with 270+ curated query templates.
The system extracts structured biological facts from paper abstracts, builds causal knowledge graphs, and identifies intervention points through network analysis.
Process:
✅ Zero Hallucinations: Every intervention backed by paper citations (PMID), molecular targets (UniProt), compounds (ChEMBL), and clinical trials (NCT)
Advanced entity recognition using BioBERT (d4data/biomedical-ner-all, 266MB model) for PhD-level biological entity extraction.
Entity Types:
Performance:
Every reasoning chain validated against live databases: UniProt (proteins), ChEMBL (compounds), ClinicalTrials.gov (trials).
Example Reasoning Chain:
System detects research intent (therapeutic/diagnostic/mechanistic) and adjusts query strategy dynamically.
Therapeutic Intent
45 queries: therapies (15) + trials (7) + cross-domain (10) + methods (8) + pathophysiology (5)
Diagnostic Intent
30 queries: methods + trials + pathophysiology focus
Mechanistic Intent
23 queries: pathophysiology + methods only
Navigate to the Discovery page and enter your medical research question. Select the medical domain (Diabetes, Oncology, Cardiology, Neurology, etc.) or let the AI detect it automatically. The system will detect your research intent (therapeutic/diagnostic/mechanistic) and adjust the query strategy accordingly.
The Exploration Agent executes 45-55 curated queries across 5 layers: pathophysiology, therapies, cross-domain innovations, methods, and clinical trials. Domain gates filter papers by medical specialty and research endpoints.
Output: 500-600 papers retrieved → Domain/Endpoint filtering → 20 CORE papers selected for deep analysis
BioBERT analyzes abstracts to extract structured biological facts (subject → predicate → object). DeepSeek validates relationships and mechanisms.
Example: "IL-2 → activates → regulatory T cells" (PMID:12345678, confidence: 0.92)
Output: 5-10 high-confidence biological facts with citations
Facts are assembled into a causal knowledge graph. Network analysis identifies key intervention points through centrality metrics.
Graph: 8-10 entities, 5-7 causal relationships
Interventions: 3-5 targets identified (top centrality nodes)
Each intervention validated against real databases to generate reasoning chains with molecular evidence.
Process:
Synthesis and Validation Agents analyze the evidence corpus, reasoning chains, and cross-domain patterns to identify unexpected connections and research gaps.
Integration and Innovation Agents propose novel hypotheses grounded in the causal graph and validated interventions. Reality Check Agent assesses practical feasibility with honest AMBER/RED flags when appropriate.
Confidence Agent applies 5D scoring (Novelty, Feasibility, Impact, Evidence, Risk). Documentation Agent compiles results with transparent reasoning chains, causal graphs, and intervention analysis.
Final Review Agent validates all claims against citations. Results include ranked hypotheses, causal reasoning visualization, molecular targets, clinical trial data, implementation roadmaps, and honest risk assessments.
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.
Complete research automation from topic analysis to final paper generation, requiring minimal human intervention.
Supports complex research projects with up to 2-hour processing time for comprehensive literature analysis and synthesis.
AI analyzes topic and creates comprehensive research strategy
Multi-database search with smart paper filtering
Deep analysis of selected papers and evidence extraction
Synthesis of evidence into coherent research narrative
Generation of publication-ready research paper
Final validation and quality assurance
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.
State-of-the-art AI model generates custom Python code (matplotlib, seaborn, statsmodels) for publication-quality visualizations at 300 DPI.
All AI-generated code runs in isolated subprocess with 30-second timeout and restricted system access for maximum security.
From basic comparisons (t-test, ANOVA) to advanced analyses (regression, survival analysis, meta-analysis, mixed models).
Complete analysis exported as Markdown with embedded base64 plots, ready for PDF/HTML conversion and publication.
Import your dataset using one of two methods:
Examine the first 20 rows of your dataset to verify data integrity before analysis:
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.
Select the appropriate test based on your research question. Tests are organized into Basic (traditional methods) and Advanced (AI-powered) categories.
Use when: Comparing means between two groups
Examples:
Statistical method: Independent t-test, Cohen's d effect size
Use when: Comparing means across 3+ groups
Examples:
Statistical method: One-way ANOVA, Tukey HSD post-hoc
Use when: Exploring correlations between variables
Examples:
Statistical method: Pearson correlation, scatter plots
Use when: Generating summary statistics
Examples:
Statistical method: Mean, SD, ranges, frequencies
AI REQUIRED These tests automatically enable the AI Agent and generate custom analysis code
Capabilities:
Libraries: statsmodels, scikit-learn
Capabilities:
Libraries: lifelines
Capabilities:
Libraries: meta-analysis, scipy
Capabilities:
Libraries: statsmodels.stats.power
Capabilities:
Libraries: statsmodels MixedLM
When ENABLED (default for advanced tests):
When DISABLED (basic tests only):
Auto-Enable Feature: When you select an advanced test, the AI Agent automatically enables itself (mandatory for these analyses).
Click the magic button to execute your analysis. The system will:
Data Validation
Checks data types, handles missing values, validates structure
Statistical Computation
Runs appropriate statistical tests, calculates p-values, effect sizes
AI Code Generation (if enabled)
DeepSeek R1 generates custom visualization code based on your data
Safe Code Execution
Runs code in isolated subprocess with timeout protection
Plot Capture & Interpretation
Saves plots as 300 DPI PNG, converts to base64, generates explanation
Comprehensive results display with multiple sections:
Non-technical summary of findings written for medical professionals. Explains what the statistics mean in clinical context.
Publication-quality plots at 300 DPI with:
Detailed statistical results including test statistics, p-values, effect sizes, confidence intervals, and diagnostic information.
Auto-generated text for your manuscript's Methods and Results sections, following APA/AMA style guidelines.
View the actual Python code generated by AI for transparency and reproducibility. Can be copied and run independently.
Save your complete analysis in multiple formats:
Cause: Backend not recognizing test type
Solution: Refresh page, clear browser cache, or contact admin if persists
Cause: Data formatting issues or special characters
Solution: Check for unusual characters, save as UTF-8, remove empty rows/columns
Cause: Large dataset or complex AI generation
Solution: Reduce dataset size, disable AI Agent for quick tests, or try simpler test type
Cause: Browser blocking base64 images or slow network
Solution: Check browser console for errors, disable ad blockers, refresh page
Cause: Browser download restrictions or large file size
Solution: Allow pop-ups/downloads from site, check storage space, try different browser
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.
Connect your research to your professional ORCID profile for academic recognition and proper attribution.
Download your research in multiple scientific formats optimized for publication and sharing.
Perfect format preservation with charts, equations, and interactive elements.
Academic-grade formatting for journal submissions and conferences.
Publish your research directly to Zenodo with automatic DOI assignment and perfect format preservation.
Direct links and integration with major academic repositories and platforms.
Open Science Repository
Research Data Repository
Academic Network
Quick access to submission portals for major medical and scientific journals.
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.
You must save your project first before publishing to the Public Library. The publishing process captures the saved content, not the current editor content.
Visit /public-library to browse all published research projects from the medical community.
Click on any project to view the full research content with preserved formatting, charts, and tables.
Use specialty filters to find research relevant to your medical field of interest.
Comprehensive medical analysis includes multiple structured sections:
The MedResearch AI Platform employs a clinical-grade design system that reflects the serious nature of medical research while maintaining usability and accessibility.