Use LLM APIs for radiology tasks. Also use when integrating medical LLMs (MedPaLM, MedLM, Google Health, Amazon HealthLake) for report analysis, clinical reasoning, or radiology AI workflows.

LLM for Radiology

You are an expert in medical large language models (LLMs) for radiology applications. Your role is to help users integrate and optimize LLM-based radiology workflows.

Supported LLM Platforms

Platform	Focus	Capabilities
MedPaLM/MedLM	Medical reasoning	Report analysis, QA
Google Health	Medical imaging	Multi-modal reasoning
Amazon HealthLake	Healthcare data	FHIR integration
Azure AI Health	Medical NLP	Clinical insights
Claude Health	Medical reasoning	Report analysis

Key Concepts

Medical LLM Capabilities

Report summarization
Finding extraction
Clinical reasoning
Prior study comparison
Structured data extraction
Quality assessment

Prompt Engineering

SYSTEM_PROMPT = """You are an expert radiologist assistant. 
Your role is to analyze radiology reports and provide insights.
Always be clinically accurate and evidence-based.
Prioritize patient safety in all recommendations."""

MedPaLM Integration

API Configuration

import requests
import json

MEDPALM_API = "https://generativelanguage.googleapis.com/v1beta1"

def configure_medpalm(api_key):
    """Configure MedPaLM API."""
    return {
        "base_url": MEDPALM_API,
        "api_key": api_key,
        "model": "medpalm-2"
    }

def query_medpalm(config, prompt, context=None):
    """Query MedPaLM for radiology insights."""
    url = f"{config['base_url']}/models/{config['model']}:generateContent"
    
    contents = [{"parts": [{"text": prompt}]}]
    
    if context:
        contents[0]["parts"][0]["text"] = f"Context: {context}\n\nQuestion: {prompt}"
    
    response = requests.post(
        f"{url}?key={config['api_key']}",
        headers={"Content-Type": "application/json"},
        json={
            "contents": contents,
            "generationConfig": {
                "temperature": 0.2,
                "topP": 0.8,
                "maxOutputTokens": 1024
            }
        }
    )
    
    return response.json()

Report Analysis Prompt

REPORT_ANALYSIS_PROMPT = """Analyze the following radiology report and provide:
1. Key findings summary
2. Critical findings (if any)
3. Clinical recommendations
4. Suggested follow-up

Report:
{report_text}

Respond in structured format."""

def analyze_report(config, report_text):
    """Analyze radiology report with MedPaLM."""
    prompt = REPORT_ANALYSIS_PROMPT.format(report_text=report_text)
    return query_medpalm(config, prompt)

Google Health Integration

Medical Imaging API

GOOGLE_HEALTH_API = "https://health.googleapis.com/v1"

def configure_google_health(credentials_path):
    """Configure Google Health API."""
    return {
        "base_url": GOOGLE_HEALTH_API,
        "credentials": credentials_path
    }

def medical_insights(config, study_data):
    """Get medical imaging insights."""
    response = requests.post(
        f"{config['base_url']}/projects/{config['project']}/locations:improve",
        headers={"Authorization": f"Bearer {get_token(config)}"},
        json=study_data
    )
    return response.json()

Amazon HealthLake Integration

FHIR-Based Integration

import boto3

def configure_healthlake(region="us-east-1"):
    """Configure Amazon HealthLake."""
    return {
        "client": boto3.client("healthlake", region_name=region),
        "datastore_id": None  # Set after creation
    }

def query_imaging_history(config, patient_id):
    """Query patient imaging history from HealthLake."""
    response = config["client"].search_by_range(
        AssetId=patient_id,
        SearchParameters={
            "filters": {
                "DocumentType": {"Value": "DiagnosticReport", "Type": "String"}
            }
        }
    )
    return response["Results"]

Send Imaging Results

def send_results_to_healthlake(config, patient_id, report_data):
    """Send radiology report to HealthLake."""
    config["client"].create_fhir_resource({
        "ResourceType": "DiagnosticReport",
        "subject": {"reference": f"Patient/{patient_id}"},
        "status": "final",
        "code": {"text": report_data["study_type"]},
        "conclusion": report_data["impression"]
    })

Azure AI Health

Health NLP Configuration

AZURE_ENDPOINT = "https://<resource>.cognitiveservices.azure.com"

def configure_azure_health(endpoint, api_key):
    """Configure Azure AI Health."""
    return {
        "endpoint": endpoint,
        "api_key": api_key
    }

def extract_medical_entities(config, text):
    """Extract medical entities from report."""
    response = requests.post(
        f"{config['endpoint']}/text/analytics/v3.1/entities/health",
        headers={
            "Ocp-Apim-Subscription-Key": config["api_key"],
            "Content-Type": "application/json"
        },
        json={"documents": [{"id": "1", "text": text}]}
    )
    return response.json()

Structured Data Extraction

Report to Structured Format

EXTRACTION_PROMPT = """Extract structured data from this radiology report:

Report: {report_text}

Extract and format as JSON:
{{
    "patient_id": "...",
    "study_type": "...",
    "findings": [
        {{
            "anatomy": "...",
            "finding": "...",
            "size": "...",
            "location": "..."
        }}
    ],
    "impression": "...",
    "critical_findings": [...],
    "recommendations": [...]
}}"""

def extract_structured(config, report_text):
    """Extract structured data from report."""
    prompt = EXTRACTION_PROMPT.format(report_text=report_text)
    response = query_llm(config, prompt)
    
    # Parse JSON from response
    return json.loads(extract_json(response))

Clinical Reasoning

Comparison Analysis

COMPARISON_PROMPT = """Compare these two CT reports and identify changes:

Current Report:
{current}

Prior Report:
{prior}

Identify:
1. New findings
2. Resolved findings
3. Changed findings (with details)
4. Stable findings
5. Clinical significance"""

def compare_reports(config, current, prior):
    """Compare current and prior reports."""
    prompt = COMPARISON_PROMPT.format(current=current, prior=prior)
    return query_llm(config, prompt)

Differential Diagnosis

DIFFERENTIAL_PROMPT = """Based on these imaging findings, provide differential diagnosis:

Findings: {findings}
Modality: {modality}
Clinical history: {history}

For each differential:
1. Diagnosis
2. Key supporting features
3. Most likely ranking
4. Recommended additional imaging (if needed)"""

def get_differential(config, findings, modality, history):
    """Get differential diagnosis."""
    prompt = DIFFERENTIAL_PROMPT.format(
        findings=findings,
        modality=modality,
        history=history
    )
    return query_llm(config, prompt)

Batch Processing

Bulk Report Analysis

def batch_analyze_reports(config, reports, batch_size=10):
    """Analyze multiple reports in batch."""
    results = []
    
    for i in range(0, len(reports), batch_size):
        batch = reports[i:i + batch_size]
        batch_results = []
        
        for report in batch:
            try:
                result = analyze_report(config, report["text"])
                batch_results.append({
                    "report_id": report["id"],
                    "analysis": result
                })
            except Exception as e:
                batch_results.append({
                    "report_id": report["id"],
                    "error": str(e)
                })
        
        results.extend(batch_results)
    
    return results

Best Practices

Validate outputs - Always review LLM-generated content
Use appropriate temperature - Lower (0.2-0.3) for factual analysis
Provide context - Include clinical history when available
Set confidence thresholds - Flag low-confidence responses
Monitor for hallucinations - Verify against source data

Troubleshooting

Issue	Solution
Slow responses	Use batch processing
Inaccurate output	Refine prompt with examples
Missing data	Ensure context is complete
Rate limits	Implement backoff strategy

Related Skills

radiology-report-analysis: For report analysis basics
llm-radiology-use: For LLM integration (this skill)
ai-quality-review: For output validation
guideline-integration: For evidence-based recommendations

Examples

Example 1: Analyze Report

Use MedPaLM to analyze this chest CT report

report = "CT CHEST: 2.5cm mass right upper lobe..."
analysis = analyze_report(config, report)

Example 2: Compare Studies

Compare this CT with the prior study from 3 months ago

comparison = compare_reports(
    config,
    current="Current report text...",
    prior="Prior report text..."
)

Example 3: Extract Structured Data

Extract findings from this report to structured format

structured = extract_structured(config, report_text)
# Returns JSON with findings, measurements, etc.

llm-radiology-use