---
license: mit
language:
- en
- tr
pipeline_tag: text-generation
library_name: transformers
tags:
- brain
- doctor
- health
- neuroscience
- large-language-model
- comment
- hospital
- scientific
- LLM
- diagnosis
- diseases
- medical
- biology
metrics:
- accuracy
---
# Lbai-1-preview

## Model Details

### Model Description

**Lbai-1-preview** is a specialized medical language model designed to assist with medical and healthcare-related queries, providing informative responses based on medical knowledge.

This model has been trained using advanced machine learning techniques to create a standalone, fully-integrated language model optimized for medical domain applications.

- **Developed by:** Neurazum
- **Model type:** Causal Language Model (Medical Domain)
- **Architecture:** Transformer-based decoder model
- **Language:** English
- **License:** MIT
- **Model size:** ~5GB (float16)
- **Parameters:** 2.7B parameters

## Intended Use

### Direct Use

Lbai-1-preview is designed for:

1. **Medical Question Answering**: Providing informative responses to general medical questions
2. **Medical Information Retrieval**: Helping users understand medical concepts, symptoms, and conditions
3. **Healthcare Education**: Supporting medical students and healthcare professionals with educational content
4. **Patient Education**: Offering accessible explanations of medical terms and conditions
5. **Clinical Documentation Assistance**: Helping draft medical notes and summaries (with human oversight)
6. **Medical Research Support**: Assisting in literature review and medical knowledge synthesis

### Recommended Use Cases

- Educational platforms for medical training
- Healthcare chatbots with appropriate disclaimers
- Medical information systems with human-in-the-loop verification
- Research assistants for medical literature analysis
- Clinical documentation support tools

### Use with Supervision

**IMPORTANT**: This model should **ALWAYS** be used with appropriate human supervision, especially in:
- Clinical decision-making contexts
- Patient care situations
- Medication recommendations
- Diagnosis suggestions
- Treatment planning

## Out-of-Scope Uses

### Prohibited Uses

This model should **NOT** be used for:

1. **Primary Clinical Diagnosis**: Making medical diagnoses without professional medical oversight
2. **Treatment Decisions**: Making independent treatment or medication decisions
3. **Emergency Medical Situations**: Providing guidance in life-threatening or emergency situations
4. **Replacing Healthcare Professionals**: Substituting for qualified medical practitioners
5. **Self-Medication Guidance**: Providing medication recommendations without medical supervision
6. **Critical Care Decisions**: Making decisions in intensive care or critical medical situations
7. **Legal Medical Advice**: Providing legally binding medical opinions
8. **Prescription Decisions**: Determining medication dosages or prescriptions

### High-Risk Scenarios

- **Pediatric Care**: Specialized medical care for children requires expert oversight
- **Oncology**: Cancer diagnosis and treatment planning
- **Surgery Planning**: Surgical procedures and planning
- **Mental Health Crisis**: Acute mental health emergencies
- **Rare Diseases**: Conditions requiring specialized expertise

### Ethical Boundaries

- Generating fake medical records
- Impersonating healthcare professionals
- Providing medical advice in jurisdictions where AI cannot legally do so
- Using for insurance fraud or medical billing manipulation

## Training Data

### Dataset Information

**Training Dataset Composition:**
- **Primary Source(s):** Medical textbooks, PubMed articles, NCBI
- **Dataset Size:** 4.8M token
- **Language(s):** English and Turkish
- **Domain Coverage:** Neuroscience, Neurology

**Example Dataset Types:**
- Medical literature and textbooks
- Clinical guidelines and protocols
- Patient-doctor conversation datasets
- Medical question-answer pairs
- Clinical case studies
- Medical terminology databases

## Evaluation Data

### Test Dataset

**Evaluation Dataset:**
- **Source:** PubMed articles, NCBI
- **Size:** 500K token
- **Split Ratio:** 80% train, 10% validation, 10% test

## Benchmark Results

### Medical Knowledge Tests

| Benchmark | Score |
|-----------|-------|
| MedQA | 36.53% (± 1.35%) |
| MedMCQA | 35.16% (± 0.07%)|
| MMLU-Medical (Anatomy) | 48.88% (± 0.43%) |
| MMLU-Medical (Clinical Knowledge) | 59.62% (± 0.30%) |
| MMLU-Medical (College Biology) | 58.33% (± 0.41%) |
| MMLU-Medical (College Medicine) | 54.91% (± 0.37%) |
| MMLU-Medical (Medical Genetics) | 61.00% (± 0.49%) |
| MMLU-Medical (Professional Medicine) | 50.00% (± 0.30%) |

### General Language Tests

| Benchmark | Score |
|-----------|-------|
| MMLU (Overall) | 54.26% (± 0.40%)|
| TruthfulQA | 44.48% (± 1.52%) |
| HellaSwag | 57.19% (± 0.49%) |

### Qualitative Analysis

**Strengths:**
- Strong performance on general medical questions
- Good understanding of medical terminology

**Weaknesses:**
- May struggle with rare diseases
- Limited multilingual support

## Ethical Considerations

### Medical AI Ethics

**Patient Safety First:**
This model is designed as an assistive tool, not a replacement for healthcare professionals. All outputs should be:
- Verified by qualified medical professionals before clinical use
- Used only in appropriate contexts with proper oversight
- Accompanied by clear disclaimers about AI limitations

### Bias and Fairness

**Potential Biases:**
- **Dataset Bias:** Medical literature may overrepresent certain populations or conditions
- **Language Bias:** Model may perform differently across languages or dialects
- **Demographic Bias:** Performance may vary across age groups, genders, or ethnicities
- **Geographic Bias:** Medical practices and terminology vary by region

**Mitigation Efforts:**
- Diverse evaluation datasets
- Regular bias audits
- Transparent reporting of limitations

### Privacy and Data Protection

**Training Data Privacy:**
- All training data has been processed to remove personally identifiable information (PII)
- No patient health information (PHI) from the training data can be extracted from the model

**User Privacy:**
- Users should not input sensitive personal health information
- Conversations should be treated as non-confidential unless properly secured
- Organizations deploying this model must implement appropriate privacy safeguards

### Transparency and Accountability

**Model Limitations:**
- This model can make mistakes and generate incorrect information
- Medical knowledge evolves; model may contain outdated information
- Model cannot access real-time medical databases or patient records
- Cannot perform physical examinations or diagnostic tests

**Accountability:**
- Responsibility for medical decisions remains with healthcare professionals
- Users should verify all information with qualified sources
- Feedback and error reporting mechanisms should be established

### Societal Impact

**Positive Impacts:**
- Improved access to medical information
- Support for medical education
- Assistance in underserved regions (with appropriate oversight)
- Reduced burden on healthcare systems for routine inquiries

**Potential Risks:**
- Over-reliance on AI for medical decisions
- Spread of medical misinformation if used improperly
- Reduced human interaction in healthcare
- Unequal access based on digital divide

**Recommendations:**
- Always use with professional medical oversight
- Implement strong disclaimer systems
- Provide user education on AI limitations
- Ensure equitable access to both AI tools and human healthcare

## Limitations

### Technical Limitations

1. **Knowledge Cutoff:**
   - Training data has a cutoff date Oct-2025
   - May not include latest medical research and guidelines
   - Drug information and treatment protocols may be outdated

2. **Context Window:**
   - Maximum context length: 2048 tokens
   - Cannot process very long medical documents in a single pass
   - May lose context in extended conversations

3. **Language Support:**
   - May have reduced performance in non-primary languages
   - Medical terminology translation may be imperfect

4. **Computational Requirements:**
   - Requires ~5GB VRAM for inference (float16)
   - May be slow on CPU-only systems
   - Real-time applications may need optimization

### Medical Knowledge Limitations

1. **Scope of Knowledge:**
   - Cannot access patient-specific data or medical records
   - No real-time drug interaction databases
   - Cannot perform calculations requiring patient-specific parameters

2. **Diagnostic Limitations:**
   - Cannot perform physical examinations
   - Cannot interpret medical images, lab results, or vital signs
   - Diagnosis requires comprehensive clinical assessment beyond text

3. **Specialization:**
   - May perform better in some medical specialties than others
   - Rare diseases and conditions may not be well-represented
   - Cutting-edge treatments may not be included

4. **Clinical Reasoning:**
   - Cannot replace clinical judgment and experience
   - May not consider all relevant factors in complex cases
   - Cannot account for individual patient circumstances

### Accuracy and Reliability

1. **Hallucination Risk:**
   - Model may generate plausible-sounding but incorrect information
   - Medical facts should always be verified
   - Citations may be fabricated

2. **Inconsistency:**
   - May provide different answers to similar questions
   - Response quality may vary based on phrasing
   - Temperature settings affect consistency

3. **Uncertainty Calibration:**
   - Model may not accurately express uncertainty
   - Confident-sounding responses may be incorrect
   - No built-in confidence scores

### Safety Limitations

1. **Emergency Situations:**
   - **ABSOLUTELY NOT for medical emergencies**
   - Cannot call emergency services
   - Cannot provide real-time critical care guidance

2. **Medication Safety:**
   - Cannot verify drug interactions for specific patients
   - Cannot determine appropriate dosages
   - Cannot account for allergies or contraindications

3. **Mental Health:**
   - Not designed for crisis intervention
   - Cannot provide ongoing mental health therapy
   - Should not replace mental health professionals

### Regulatory and Legal Limitations

1. **Not FDA Approved:**
   - This model is not approved as a medical device
   - Not certified for clinical decision support
   - Not validated for regulatory compliance

2. **Professional Liability:**
   - Does not establish doctor-patient relationship
   - Users responsible for verifying all information
   - Not a substitute for professional medical advice

3. **Geographic Limitations:**
   - Medical practices vary by country and region
   - May not align with local medical standards
   - Regulatory status varies by jurisdiction

### Mitigation Strategies

**For Developers:**
- Implement hallucination detection systems
- Add fact-checking layers
- Provide confidence scores when possible
- Regular model updates with new medical knowledge

**For Users:**
- Always verify critical information
- Use as a supplementary tool, not primary source
- Consult qualified healthcare professionals
- Report errors and inconsistencies

**For Organizations:**
- Implement human-in-the-loop systems
- Regular audits and quality checks
- Clear disclaimers and user warnings
- Appropriate training for staff using the model

## How to Use

### Basic Usage

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model_path = "Neurazum/Lbai-1-preview"  # Update with your actual path
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True
)

tokenizer = AutoTokenizer.from_pretrained(model_path)

# Example usage
prompt = "Patient: I have a persistent headache and fever. What could be the cause?\nDoctor:"

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=200,
        temperature=0.7,
        top_p=0.9,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```

### System Requirements

**Minimum:**
- GPU: 6GB VRAM (float16)
- RAM: 8GB
- Storage: 6GB

**Recommended:**
- GPU: 8GB+ VRAM
- RAM: 16GB+
- Storage: 10GB

### Installation

```bash
pip install torch transformers accelerate
```

## Medical Disclaimer

⚠️ **IMPORTANT MEDICAL DISCLAIMER** ⚠️

This AI model is provided for **informational and educational purposes only** and is **NOT** a substitute for professional medical advice, diagnosis, or treatment.

**Key Points:**

1. **Not Medical Advice:** The information provided by this model does not constitute medical advice and should not be relied upon for medical decisions.

2. **Consult Healthcare Professionals:** Always seek the advice of your physician or other qualified health provider with any questions you may have regarding a medical condition.

3. **Emergency Situations:** Never disregard professional medical advice or delay in seeking it because of something you have read or received from this AI model. If you think you may have a medical emergency, call your doctor or emergency services immediately.

4. **No Doctor-Patient Relationship:** Use of this model does not create a doctor-patient relationship between you and the developers or operators of this model.

5. **Accuracy Not Guaranteed:** While efforts have been made to ensure accuracy, medical information can change rapidly, and this model may contain errors or outdated information.

6. **Individual Variation:** Medical conditions and treatments vary greatly among individuals. Information that may be appropriate for one person may not be appropriate for another.

7. **Verify Information:** Always verify any medical information with qualified healthcare professionals and current medical literature.

**By using this model, you acknowledge and agree to these limitations and disclaimers.**

## Citation

If you use this model in your research or applications, please cite:

```bibtex
@misc{neurazum,
   title = {Lbai-1-preview},
   author = {Neurazum AI Department},
   year = {2025},
   url = {https://huggingface.co/Neurazum/Lbai-1-preview},
}
```

## Contact

- **Organization:** Neurazum
- **Contact Email:** contact@neurazum.com
- **Website:** neurazum.com

## Updates and Versions

### Version 1.0-preview (Current)
- Initial release
- Medical domain optimization

### Planned Updates
- [×] Additional medical domain training
- [×] Multilingual support expansion
- [×] Safety improvements
- [×] Integration of bai-Mind models that interpret EEG signals
- [×] Reasoning ability

---

**Last Updated:** 2025-11-25
**Model Version:** 1.0-preview
**Documentation Version:** 1.0