1. Introduction
Modern medicine generates a massive volume of data—lab results, diagnostic imaging, clinical documentation, patient notes. But much of this data is locked behind complexity, requiring expert interpretation that’s often inaccessible to the average person.
GalenAI is built to democratize this knowledge. By fusing machine learning with biomedical data, our goal is to provide intuitive, explainable, and powerful AI-driven tools that help people understand and act on their health information—whether they are patients, healthcare providers, or researchers.
2. Mission
To empower individuals and professionals with accurate, human-friendly medical intelligence powered by trustworthy and ethical AI.
3. Production Models
🧪 GalenLabModel v1.0
Function: Analyze structured blood and biochemistry test results to detect abnormal markers and provide clinical context.
- Detect abnormal markers (CBC, CMP, LFT, thyroid, lipids, etc.)
- Provide clinical context and risk assessment
- Suggest follow-up recommendations
Technology: Transformer-based classifier + clinical rule engine (PyTorch)
Training Data: 2.4M anonymized lab reports (MIMIC-IV, NHANES, synthetic edge cases)
Performance: F1-score abnormality detection: 0.89, Accuracy: 91.3%
Status: ✅ Live
📖 GalenExplainModel v1.0
Function: Explain medical terms and clinical jargon in plain language.
- Understands ICD-10, SNOMED, or freeform input
- Outputs simplified definitions, symptoms, and treatments
- Multi-language support: English, Russian (Spanish beta)
Technology: Fine-tuned LLM with retrieval support (SNOMED CT, Mayo Clinic, MedlinePlus)
Training Corpus: 15M+ entries, factual accuracy 92% (internal eval)
Status: ✅ Live
💬 GalenQAModel v1.0
Function: Answer general medical questions in natural language.
- Structured and sourced answers
- Covers conditions, medications, symptoms
Technology: LLM ensemble with PubMed-based retrieval
Status: ✅ Live
4. Beta Models (Coming Soon)
❤️ GalenCardioModel beta
Function: Upload an ECG image and receive AI-generated interpretation.
- Recognize rhythm disorders, ischemic patterns
- Highlight potential abnormalities
Technology: CNN + RNN hybrid
Training Data: PTB-XL, MIT-BIH, Chapman ECG datasets
Accuracy (preliminary): 84% on 12-class classification
Status: 🚧 Beta
📩 Request early access: [email protected]
🌿 GalenDermaModel beta
Function: Analyze skin photos or dermatoscopic images to identify possible skin conditions.
- Risk classification (benign, moderate, critical)
- Top-3 condition prediction
Technology: ResNet-50 + attention heads
Training Data: HAM10000, ISIC 2019-2020, DermNet curated set
Accuracy (preliminary): 87% balanced accuracy
Status: 🚧 Beta
📩 Request early access: [email protected]
5. Protein Models
🧬 Protein Structure Predictor
Predict the 3D structure of proteins from sequence using state-of-the-art AI.
🔄 Mutation Impact Analyzer
Assess the effect of point mutations on protein stability and function.
📍 Binding Site Predictor
Identify ligand or drug binding sites on protein structures.
6. Protein Tools & Analytics
🔎 Protein Similarity Search
Find similar protein sequences or structures using deep embedding comparison.
🧩 Sequence Annotator
Automatically annotate domains, motifs, and functional sites in protein sequences.
7. Technical Stack & Architecture
Our platform is built on a scalable microservices architecture with Python and Rust backend, React frontend, and Kubernetes-based deployment. Core AI models use PyTorch and TensorFlow.
8. Technical Stack
- Frameworks: PyTorch, TensorFlow, Hugging Face Transformers
- Deployment: Docker, FastAPI, AWS Lambda, GPU inference cluster
- Data Flow: Client → Encrypted API → Model → Output → Purge (no storage)
- Security: TLS 1.3, AES-256 encryption, Zero-retention policy unless consent is granted
9. Privacy & Ethics
- ✅ No diagnoses: Models support, not replace clinicians
- ✅ User control: Nothing is stored without consent
- ✅ Transparent logic: Model limitations disclosed
- ✅ Regulatory alignment: Compliant with HIPAA, GDPR, ISO/IEC 27001
We are committed to preventing medical misinformation and ensuring our tools only aid, never mislead.
10. Use Cases
- 👩⚕️ Patients: Understand lab results or unfamiliar diagnoses
- 🧑⚕️ Doctors: Speed up pre-screening and reduce explanation time
- 🧪 Researchers: Leverage structured data for large-scale studies
- 🧑💻 Developers: Coming soon — integrate AI models into apps via API
11. Access & Collaboration
You can currently request early access or collaborate via:
- 📬 Beta access or pilots: [email protected]
- 🤝 Partnerships: [email protected]
We’re onboarding research institutions, startups, and clinics actively.
12. Roadmap
| Milestone | Timeline |
|---|
| Finalize ECG & Derma beta | Q2 2025 |
| Launch GalenAI web app | Q3 2025 |
| Public API release | Q4 2025 |
| Expand to radiology + EHR NLP | 2026 |
13. Contact Us
*Disclaimer: GalenAI is an assistive tool and does not provide medical diagnosis, treatment, or replace professional medical judgment.*