Medical LLM & VLM

Access production-ready medical large language models (LLMs) and vision-language models (VLMs) fine-tuned on clinical corpora for accurate healthcare AI applications.

Medical-Grade AI Models for Clinical Reasoning

Pre-trained LLMs and VLMs fine-tuned on medical literature, clinical notes, and radiology/pathology images for superior accuracy in healthcare contexts.

Overview

General-purpose LLMs like GPT-4 and Claude have broad knowledge but often hallucinate medical facts, confuse drug dosages, or misinterpret clinical terminology. Medical-specific models address these limitations through:

Domain-specific pre-training on PubMed, clinical guidelines, and medical textbooks
Fine-tuning on clinical tasks like diagnosis support, ICD coding, and clinical note generation
Specialized tokenization for medical terminology and abbreviations
Validation on clinical benchmarks (MedQA, PubMedQA, MIMIC-III)
Safety guardrails to prevent dangerous recommendations

Available Models

🩺

Clinical-GPT-7B

General Clinical Reasoning

7B parameters, fine-tuned on MIMIC-III
Diagnosis support and differential generation
Clinical note summarization
Treatment recommendation analysis
MedQA accuracy: 67.8%

🧬

BioMedLM-13B

Biomedical Literature & Research

13B parameters, trained on PubMed abstracts
Scientific literature summarization
Clinical trial matching
Pharmacology and mechanism of action
PubMedQA accuracy: 72.3%

📋

Clinical-Coder-3B

Medical Coding & Documentation

3B parameters, optimized for ICD/CPT coding
Automated ICD-10-CM/PCS assignment
CPT procedure code suggestion
DRG prediction
ICD coding F1: 0.89

🔬

Radiology-VLM-8B

Medical Image Interpretation

8B parameters, vision-language model
Chest X-ray, CT, MRI interpretation
Radiology report generation
Finding localization and measurement
MIMIC-CXR CheXpert F1: 0.82

🧪

Pathology-VLM-12B

Histopathology Analysis

12B parameters, trained on WSI datasets
Tumor classification and grading
Biomarker identification (ER, PR, HER2)
Pathology report generation
Tumor detection AUROC: 0.94

💬

Patient-Facing-LLM-7B

Patient Communication & Education

7B parameters, trained on patient education materials
Translates medical jargon to layman terms
Medication instruction generation
Discharge summary simplification
Reading level: 6th-8th grade

Model Comparison

When to Use Each Model

📊 Clinical Reasoning Tasks

Recommended: Clinical-GPT-7B

Diagnosis support, differential generation, treatment analysis, clinical decision support

📚 Literature Review & Research

Recommended: BioMedLM-13B

PubMed summarization, clinical trial matching, drug mechanism explanation, research hypothesis generation

🏥 Medical Coding & Billing

Recommended: Clinical-Coder-3B

ICD-10 code assignment, CPT code suggestion, DRG prediction, coding validation

🖼️ Radiology Imaging

Recommended: Radiology-VLM-8B

X-ray/CT/MRI interpretation, finding localization, report generation, image-based triage

🔬 Pathology Analysis

Recommended: Pathology-VLM-12B

Histopathology classification, tumor grading, biomarker identification, slide-level diagnosis

👥 Patient Communication

Recommended: Patient-Facing-LLM-7B

Patient education, discharge instructions, medication explanations, symptom assessment chatbots

API Usage

Text Generation with Medical LLMs

Endpoint: POST /v1/models/{model_id}/generate

Request:

{
  "model": "clinical-gpt-7b",
  "prompt": "A 65-year-old male presents with sudden onset severe chest pain radiating to the left arm, diaphoresis, and nausea. Vital signs: BP 160/95, HR 110, SpO2 94% on room air. ECG shows ST-segment elevation in leads II, III, and aVF. What is the most likely diagnosis and immediate management?",
  "max_tokens": 500,
  "temperature": 0.3,
  "top_p": 0.9,
  "stop_sequences": ["\n\n"]
}

Response:

{
  "model": "clinical-gpt-7b",
  "generated_text": "Most Likely Diagnosis: Acute Inferior Wall ST-Elevation Myocardial Infarction (STEMI)\n\nImmediate Management:\n1. Activate cardiac catheterization lab for emergent PCI (door-to-balloon time <90 minutes)\n2. Administer dual antiplatelet therapy: Aspirin 324mg PO + Ticagrelor 180mg PO or Prasugrel 60mg PO\n3. Anticoagulation: Heparin bolus 60 units/kg (max 4000 units) followed by infusion 12 units/kg/hr\n4. Oxygen if SpO2 <90% (currently 94%, so supplemental O2 PRN)\n5. Nitroglycerin sublingual 0.4mg if systolic BP >90mmHg (caution: inferior MI may have RV involvement - check right-sided leads)\n6. Morphine 2-4mg IV for pain relief\n7. High-intensity statin: Atorvastatin 80mg PO\n8. Beta-blocker: Metoprolol 25-50mg PO once hemodynamically stable\n\nNote: Given inferior wall STEMI, obtain right-sided ECG (V3R, V4R) to assess for RV infarction, which would contraindicate nitrates and require aggressive fluid resuscitation.",
  "finish_reason": "stop",
  "usage": {
    "prompt_tokens": 89,
    "completion_tokens": 247,
    "total_tokens": 336
  }
}

Vision-Language Model (VLM) Usage

Endpoint: POST /v1/models/{model_id}/analyze_image

Request:

{
  "model": "radiology-vlm-8b",
  "image": "base64_encoded_chest_xray_image_data_here...",
  "prompt": "Describe all findings in this chest X-ray and provide a structured radiology report.",
  "temperature": 0.2,
  "max_tokens": 400
}

Response:

{
  "model": "radiology-vlm-8b",
  "analysis": {
    "findings": [
      {
        "finding": "Right lower lobe opacity",
        "location": {"x": 420, "y": 380, "width": 85, "height": 95},
        "confidence": 0.89,
        "severity": "moderate"
      },
      {
        "finding": "Blunting of right costophrenic angle",
        "location": {"x": 450, "y": 520, "width": 60, "height": 40},
        "confidence": 0.76,
        "severity": "mild"
      }
    ],
    "report": "FINDINGS:\n\n1. Right lower lobe opacity measuring approximately 8.5 x 9.5 cm, concerning for pneumonia vs. atelectasis\n2. Mild blunting of the right costophrenic angle, suggesting small pleural effusion\n3. Cardiac silhouette within normal limits\n4. No pneumothorax identified\n5. Visualized bony structures unremarkable\n\nIMPRESSION:\n1. Right lower lobe opacity, most consistent with community-acquired pneumonia. Small right pleural effusion.\n2. Recommend clinical correlation and follow-up imaging after treatment to ensure resolution.\n\nRECOMMENDATIONS:\nConsider lateral view or CT chest if clinically indicated for further characterization."
  },
  "usage": {
    "prompt_tokens": 1250,
    "completion_tokens": 178,
    "total_tokens": 1428
  }
}

Model Parameters

Temperature

Controls randomness in output. Lower = more deterministic, higher = more creative.

Recommendations:

0.1-0.3: Clinical decision support, diagnosis, coding (high accuracy needed)
0.5-0.7: Clinical note generation, patient education (balance accuracy and variety)
0.8-1.0: Creative tasks like patient education content generation (NOT recommended for clinical reasoning)

Top-P (Nucleus Sampling)

Alternative to temperature. Considers only the most probable tokens whose cumulative probability exceeds p.

Recommendations:

0.9: Default for most clinical tasks
0.95: When you want slightly more diverse outputs
0.85: When you need very focused, conservative outputs

Max Tokens

Maximum length of generated response.

Recommendations:

100-200: Short answers (yes/no, simple coding tasks)
300-500: Standard clinical reasoning or report generation
800-1000: Long-form documentation (discharge summaries, consultation notes)

Stop Sequences

Tokens that terminate generation early.

Common medical stop sequences:

["\n\n", "---", "END OF REPORT"] for structured reports
["Patient:", "ADDENDUM:"] to prevent model from generating additional sections

Prompt Engineering for Medical LLMs

Zero-Shot Prompting

Basic prompt without examples:

Prompt: "What is the first-line treatment for uncomplicated urinary tract infection in a non-pregnant adult female?"

Response: "First-line treatment for uncomplicated UTI in non-pregnant women:
- Nitrofurantoin 100mg BID x 5 days, OR
- Trimethoprim-sulfamethoxazole DS BID x 3 days (if local resistance <20%), OR
- Fosfomycin 3g single dose

Avoid fluoroquinolones as first-line due to resistance and side effect concerns."

Few-Shot Prompting

Provide examples to guide the model:

Prompt: "Convert clinical notes to ICD-10 codes:

Example 1:
Note: 'Patient has poorly controlled Type 2 Diabetes with diabetic retinopathy'
Codes: E11.65 (Type 2 diabetes with hyperglycemia), E11.319 (Type 2 diabetes with unspecified diabetic retinopathy)

Example 2:
Note: 'Acute exacerbation of COPD, admitted for IV steroids and oxygen'
Codes: J44.1 (COPD with acute exacerbation), J44.0 (COPD with acute lower respiratory infection)

Now code this note:
Note: 'Patient with hypertensive heart disease and chronic kidney disease stage 3'"

Response: "I13.2 (Hypertensive heart and chronic kidney disease with heart failure and with stage 1 through stage 4 chronic kidney disease, or unspecified chronic kidney disease), N18.3 (Chronic kidney disease, stage 3)"

Chain-of-Thought Prompting

Ask the model to explain its reasoning:

Prompt: "A 28-year-old pregnant woman at 32 weeks gestation presents with right upper quadrant pain, headache, and blurred vision. Labs show platelets 85,000, AST 250, ALT 220, LDH 600. Let's think through this step-by-step to reach a diagnosis."

Response: "Step 1: Identify key clinical features
- Pregnant at 32 weeks (third trimester)
- RUQ pain + neurological symptoms (headache, blurred vision)
- Thrombocytopenia (platelets <100,000)
- Elevated liver enzymes (transaminitis)
- Elevated LDH (marker of hemolysis)

Step 2: Recognize the syndrome
This constellation suggests a hypertensive disorder of pregnancy with end-organ damage.

Step 3: Apply diagnostic criteria
The combination of:
- Hemolysis (elevated LDH)
- Elevated Liver enzymes (AST/ALT >2x normal)
- Low Platelets (<100,000)

...fits the HELLP syndrome criteria.

Diagnosis: HELLP Syndrome (variant of severe preeclampsia)

Immediate Management:
- Blood pressure check (likely elevated)
- Magnesium sulfate for seizure prophylaxis
- Corticosteroids for fetal lung maturity
- Plan for delivery (definitive treatment)"

Safety & Validation

⚠️ Clinical Validation Required

All model outputs must be reviewed by qualified healthcare professionals before clinical use. Models are decision support tools, not autonomous decision makers.

🚫 Hallucination Detection

Models include confidence scores for factual claims. Outputs with low confidence (<0.7) are flagged for human review.

📊 Performance Monitoring

Continuous monitoring of model accuracy on held-out test sets. Models are retrained quarterly with updated medical knowledge.

🔒 Safety Guardrails

Models refuse to provide advice on life-threatening emergencies ("Call 911"), controlled substances without context, or experimental treatments.

Performance Benchmarks

Clinical Reasoning (Clinical-GPT-7B)

Benchmark	Accuracy	Notes
MedQA (USMLE-style)	67.8%	4-way multiple choice medical questions
PubMedQA	71.2%	Answering questions from PubMed abstracts
MIMIC-III Diagnosis	73.5%	Predicting primary diagnosis from clinical notes

Medical Coding (Clinical-Coder-3B)

Task	F1 Score	Notes
ICD-10-CM Assignment	0.89	Top-1 accuracy on diagnosis codes
CPT Code Suggestion	0.82	Procedure code prediction
DRG Classification	0.91	Medicare Severity-DRG assignment

Radiology (Radiology-VLM-8B)

Finding	AUROC	Dataset
Pneumonia	0.87	MIMIC-CXR
Pleural Effusion	0.91	CheXpert
Pneumothorax	0.89	NIH ChestX-ray14
Cardiomegaly	0.85	MIMIC-CXR

Integration with MCP Tools

Medical LLMs work seamlessly with MCP tools for agentic workflows:

Example: Automated Diagnosis Support Agent

# Pseudo-code for agent that combines LLM + MCP tools

User provides clinical note
Agent calls extract_clinical_entities (MCP tool) to identify symptoms, vitals
Agent calls Clinical-GPT-7B with structured data to generate differential diagnosis
Agent calls search_terminology (MCP tool) to get ICD-10 codes for each diagnosis
Agent calls check_drug_interactions (MCP tool) to validate proposed treatment
Agent returns formatted response with diagnosis + codes + treatment plan

Cost & Pricing

💰 Token-Based Pricing

All models are billed per 1,000 tokens (approximately 750 words):

Clinical-Coder-3B: $0.002 per 1K tokens
Clinical-GPT-7B: $0.006 per 1K tokens
BioMedLM-13B: $0.012 per 1K tokens
Radiology-VLM-8B: $0.025 per image + $0.008 per 1K tokens
Pathology-VLM-12B: $0.050 per image + $0.015 per 1K tokens

Enterprise volume discounts available for >10M tokens/month.

Best Practices

💡 Medical LLM Best Practices

Use medical-specific models: Don't use general LLMs for clinical tasks – medical models are 15-30% more accurate
Set low temperature: Use 0.1-0.3 for clinical reasoning to minimize hallucinations
Provide context: Include patient demographics, relevant history, and specific question for better responses
Validate outputs: Always have qualified clinicians review AI-generated diagnoses or treatment plans
Monitor confidence: Flag low-confidence outputs (<0.7) for additional human review
Combine with tools: Use MCP tools for structured data extraction, terminology lookup, and validation
Update regularly: Medical knowledge evolves – retrain or switch to updated model versions quarterly

Next Steps

MCP Agents Setup MCP Tools Catalog Platform REST API Building Agents Overview

Medical-Grade AI Models for Clinical Reasoning

Overview​

Available Models​

Clinical-GPT-7B

BioMedLM-13B

Clinical-Coder-3B

Radiology-VLM-8B

Pathology-VLM-12B

Patient-Facing-LLM-7B

Model Comparison​

When to Use Each Model​

📊 Clinical Reasoning Tasks

📚 Literature Review & Research

🏥 Medical Coding & Billing

🖼️ Radiology Imaging

🔬 Pathology Analysis

👥 Patient Communication

API Usage​

Text Generation with Medical LLMs​

Vision-Language Model (VLM) Usage​

Model Parameters​

Temperature​

Top-P (Nucleus Sampling)​

Max Tokens​

Stop Sequences​

Prompt Engineering for Medical LLMs​

Zero-Shot Prompting​

Few-Shot Prompting​

Chain-of-Thought Prompting​

Safety & Validation​

⚠️ Clinical Validation Required

🚫 Hallucination Detection

📊 Performance Monitoring

🔒 Safety Guardrails

Performance Benchmarks​

Clinical Reasoning (Clinical-GPT-7B)​

Medical Coding (Clinical-Coder-3B)​

Radiology (Radiology-VLM-8B)​

Integration with MCP Tools​

Cost & Pricing​

💰 Token-Based Pricing

Best Practices​

💡 Medical LLM Best Practices

Next Steps​

Overview

Available Models

Model Comparison

When to Use Each Model

API Usage

Text Generation with Medical LLMs

Vision-Language Model (VLM) Usage

Model Parameters

Temperature

Top-P (Nucleus Sampling)

Max Tokens

Stop Sequences

Prompt Engineering for Medical LLMs

Zero-Shot Prompting

Few-Shot Prompting

Chain-of-Thought Prompting

Safety & Validation

Performance Benchmarks

Clinical Reasoning (Clinical-GPT-7B)

Medical Coding (Clinical-Coder-3B)

Radiology (Radiology-VLM-8B)

Integration with MCP Tools

Cost & Pricing

Best Practices

Next Steps