Healthcare AI Systems

import torch
import torch.nn as nn
import torchvision.transforms as transforms
from torchvision.models import densenet121
import numpy as np
from PIL import Image
import cv2
from typing import Dict, List, Tuple
import logging
import warnings

class MedicalImagingAI:
    """
    Production-ready medical imaging AI system
    
    CRITICAL: This is for educational purposes only.
    Production use requires clinical validation and regulatory approval.
    """
    
    def __init__(self, model_path: str, device: str = 'cuda'):
        self.device = torch.device(device if torch.cuda.is_available() else 'cpu')
        self.model = self._load_medical_model(model_path)
        self.preprocessing = self._get_medical_transforms()
        
        # Enable comprehensive logging for medical applications
        self.logger = self._setup_medical_logging()
        
        # Safety checks
        self._validate_model_integrity()
        
    def _load_medical_model(self, model_path: str) -> nn.Module:
        """Load pre-trained medical imaging model with safety checks"""
        
        # For chest X-ray analysis (example)
        model = densenet121(pretrained=False)
        model.classifier = nn.Linear(model.classifier.in_features, 14)  # 14 pathologies
        
        # Load validated model weights
        checkpoint = torch.load(model_path, map_location=self.device)
        model.load_state_dict(checkpoint['model_state_dict'])
        
        model.to(self.device)
        model.eval()
        
        # Verify model hash for integrity
        model_hash = self._calculate_model_hash(model)
        expected_hash = checkpoint.get('model_hash')
        if expected_hash and model_hash != expected_hash:
            raise ValueError("Model integrity check failed - potential corruption")
            
        return model
    
    def _get_medical_transforms(self) -> transforms.Compose:
        """Medical image preprocessing with DICOM compatibility"""
        return transforms.Compose([
            transforms.Resize(224),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            transforms.Normalize(
                mean=[0.485, 0.456, 0.406],  # ImageNet normalization
                std=[0.229, 0.224, 0.225]
            )
        ])
    
    def analyze_medical_image(self, 
                            image_path: str, 
                            patient_id: str = None,
                            study_id: str = None) -> Dict:
        """
        Analyze medical image with comprehensive safety protocols
        
        Args:
            image_path: Path to medical image (DICOM, PNG, JPG)
            patient_id: De-identified patient identifier
            study_id: Medical study identifier
            
        Returns:
            Analysis results with confidence scores and safety flags
        """
        
        # Input validation
        if not self._validate_image_input(image_path):
            raise ValueError("Invalid medical image input")
        
        try:
            # Load and preprocess medical image
            image = self._load_medical_image(image_path)
            processed_image = self.preprocessing(image).unsqueeze(0).to(self.device)
            
            # Model inference with uncertainty quantification
            with torch.no_grad():
                # Enable dropout for uncertainty estimation
                self.model.train()  # Enable dropout layers
                
                # Multiple forward passes for uncertainty
                predictions = []
                for _ in range(10):  # Monte Carlo dropout
                    output = self.model(processed_image)
                    predictions.append(torch.sigmoid(output).cpu().numpy())
                
                self.model.eval()  # Return to eval mode
                
                # Calculate mean and uncertainty
                mean_pred = np.mean(predictions, axis=0)[0]
                std_pred = np.std(predictions, axis=0)[0]
            
            # Map to medical conditions
            pathology_labels = [
                'Atelectasis', 'Cardiomegaly', 'Effusion', 'Infiltration',
                'Mass', 'Nodule', 'Pneumonia', 'Pneumothorax',
                'Consolidation', 'Edema', 'Emphysema', 'Fibrosis',
                'Pleural_Thickening', 'Hernia'
            ]
            
            results = {}
            critical_findings = []
            
            for i, pathology in enumerate(pathology_labels):
                confidence = float(mean_pred[i])
                uncertainty = float(std_pred[i])
                
                results[pathology] = {
                    'probability': confidence,
                    'uncertainty': uncertainty,
                    'risk_level': self._assess_risk_level(confidence, uncertainty),
                    'clinical_significance': self._get_clinical_significance(pathology, confidence)
                }
                
                # Flag critical findings
                if confidence > 0.7 and pathology in ['Pneumothorax', 'Mass', 'Pneumonia']:
                    critical_findings.append(pathology)
            
            # Generate clinical report
            report = self._generate_clinical_report(results, critical_findings)
            
            # Log medical analysis
            self._log_medical_analysis(patient_id, study_id, results, critical_findings)
            
            return {
                'analysis_id': self._generate_analysis_id(),
                'timestamp': self._get_timestamp(),
                'patient_id': patient_id,
                'study_id': study_id,
                'pathology_analysis': results,
                'critical_findings': critical_findings,
                'clinical_report': report,
                'quality_metrics': self._assess_image_quality(processed_image),
                'disclaimer': self._get_medical_disclaimer()
            }
            
        except Exception as e:
            self.logger.error(f"Medical analysis failed: {str(e)}")
            return {
                'error': True,
                'message': 'Analysis failed - manual review required',
                'timestamp': self._get_timestamp()
            }
    
    def _assess_risk_level(self, confidence: float, uncertainty: float) -> str:
        """Assess clinical risk level based on AI confidence and uncertainty"""
        if uncertainty > 0.3:  # High uncertainty
            return 'uncertain_requires_review'
        elif confidence > 0.8:
            return 'high_confidence'
        elif confidence > 0.5:
            return 'moderate_confidence'
        else:
            return 'low_confidence'
    
    def _generate_clinical_report(self, results: Dict, critical_findings: List) -> str:
        """Generate structured clinical report"""
        report = "AI-ASSISTED RADIOLOGICAL ANALYSIS\n"
        report += "=" * 40 + "\n\n"
        
        if critical_findings:
            report += "CRITICAL FINDINGS DETECTED:\n"
            for finding in critical_findings:
                prob = results[finding]['probability']
                report += f"- {finding}: {prob:.2%} confidence\n"
            report += "\nIMMEDIATE CLINICAL REVIEW REQUIRED\n\n"
        
        report += "DETAILED ANALYSIS:\n"
        for pathology, data in results.items():
            if data['probability'] > 0.3:  # Only report significant findings
                report += f"- {pathology}: {data['probability']:.2%} "
                report += f"(Risk: {data['risk_level']})\n"
        
        report += "\n" + self._get_medical_disclaimer()
        return report
    
    def _get_medical_disclaimer(self) -> str:
        """Standard medical AI disclaimer"""
        return '''
IMPORTANT MEDICAL DISCLAIMER:
This AI analysis is for informational purposes only and should not be used 
as a substitute for professional medical judgment. All findings require 
verification by qualified healthcare professionals. The AI system has not 
been clinically validated for diagnostic use.
        '''.strip()
    
    def validate_for_clinical_use(self) -> Dict:
        """Comprehensive validation for clinical deployment"""
        validation_results = {
            'model_integrity': self._check_model_integrity(),
            'performance_metrics': self._validate_performance_metrics(),
            'bias_assessment': self._assess_algorithmic_bias(),
            'regulatory_compliance': self._check_regulatory_compliance(),
            'safety_protocols': self._validate_safety_protocols()
        }
        
        # Overall approval status
        validation_results['approved_for_clinical_use'] = all(
            validation_results.values()
        )
        
        return validation_results

# Example clinical integration
class ClinicalWorkflowIntegration:
    """Integration with hospital systems and clinical workflows"""
    
    def __init__(self, imaging_ai: MedicalImagingAI):
        self.imaging_ai = imaging_ai
        self.hl7_client = self._init_hl7_integration()
        self.pacs_client = self._init_pacs_integration()
        
    def process_radiology_study(self, study_id: str) -> Dict:
        """Process incoming radiology study from PACS"""
        
        # Retrieve study from PACS
        study_data = self.pacs_client.get_study(study_id)
        
        # Process each image in study
        results = []
        for image_path in study_data['images']:
            analysis = self.imaging_ai.analyze_medical_image(
                image_path=image_path,
                patient_id=study_data['patient_id'],
                study_id=study_id
            )
            results.append(analysis)
        
        # Generate consolidated report
        consolidated_report = self._consolidate_study_results(results)
        
        # Send to radiologist worklist if critical findings
        if self._has_critical_findings(results):
            self._prioritize_study(study_id, consolidated_report)
        
        # Update hospital information system
        self._update_his_with_ai_results(study_id, consolidated_report)
        
        return consolidated_report