# KI-gestützte Bildanalyse für Radiologie
import azure.cognitiveservices.vision.customvision.training as cv_training
import azure.cognitiveservices.vision.customvision.prediction as cv_prediction
from azure.cognitiveservices.vision.customvision.training.models import ImageFileCreateBatch, ImageFileCreateEntry

class MedicalImageAI:
    def __init__(self, azure_config):
        self.trainer = cv_training.CustomVisionTrainingClient(
            azure_config['training_key'],
            endpoint=azure_config['training_endpoint']
        )
        self.predictor = cv_prediction.CustomVisionPredictionClient(
            azure_config['prediction_key'],
            endpoint=azure_config['prediction_endpoint']
        )

    def analyze_chest_xray(self, image_path, patient_context):
        """
        KI-gestützte Röntgenanalyse für Thorax-Aufnahmen
        """
        # Bildanalyse mit spezialisiertem Modell
        analysis_result = self.predictor.classify_image(
            project_id="chest-xray-classifier",
            published_name="ChestXRayModel",
            image_data=open(image_path, 'rb').read()
        )

        findings = []
        confidence_threshold = 0.7

        for prediction in analysis_result.predictions:
            if prediction.probability > confidence_threshold:
                finding = {
                    'condition': prediction.tag_name,
                    'confidence': prediction.probability,
                    'severity': self.assess_severity(prediction),
                    'urgency': self.determine_urgency(prediction),
                    'recommended_action': self.recommend_action(prediction)
                }
                findings.append(finding)

        # Medizinische Interpretation
        interpretation = {
            'primary_findings': findings,
            'differential_diagnosis': self.generate_differential_diagnosis(findings),
            'recommendation': self.generate_clinical_recommendation(findings, patient_context),
            'requires_specialist_review': any(f['urgency'] == 'high' for f in findings),
            'follow_up_required': self.assess_follow_up_need(findings)
        }

        return {
            'analysis_id': self.generate_analysis_id(),
            'patient_id': patient_context['patient_id'],
            'image_analysis': interpretation,
            'confidence_score': max(f['confidence'] for f in findings) if findings else 0,
            'processing_time': '< 30 seconds',
            'compliance_note': 'Analyseergebnis dient nur der Unterstützung - finale Diagnose durch Arzt erforderlich'
        }

    def analyze_ct_scan(self, dicom_series, analysis_type='thorax'):
        """
        CT-Scan Analyse mit 3D-Rekonstruktion
        """
        # DICOM-Serie verarbeiten
        volume_data = self.load_dicom_series(dicom_series)

        # 3D-Segmentierung
        segmentation_result = self.perform_3d_segmentation(volume_data, analysis_type)

        # Volumetrie und Messungen
        measurements = self.calculate_volumetric_measurements(segmentation_result)

        # Pathologie-Erkennung
        pathology_analysis = self.detect_pathologies(segmentation_result, analysis_type)

        return {
            'segmentation_quality': segmentation_result['quality_score'],
            'volumetric_measurements': measurements,
            'detected_pathologies': pathology_analysis,
            'recommended_protocols': self.suggest_follow_up_protocols(pathology_analysis),
            'radiologist_review_priority': self.assign_review_priority(pathology_analysis)
        }

    def generate_radiology_report(self, analysis_results, template_type='structured'):
        """
        Automatische Generierung strukturierter Radiologie-Befunde
        """
        if template_type == 'structured':
            report_template = {
                'technique': self.describe_imaging_technique(analysis_results),
                'findings': self.format_findings_description(analysis_results),
                'impression': self.generate_impression(analysis_results),
                'recommendations': self.format_recommendations(analysis_results)
            }

        # Medizinische Terminologie validieren
        validated_report = self.validate_medical_terminology(report_template)

        return {
            'structured_report': validated_report,
            'confidence_indicators': self.add_confidence_indicators(validated_report),
            'requires_physician_review': True,  # Immer erforderlich
            'estimated_reading_time': '2-3 Minuten'
        }

# Prädiktive Gesundheitsanalytik
class PredictiveHealthcareAI:
    def __init__(self):
        self.risk_models = {
            'sepsis': SepsisRiskModel(),
            'readmission': ReadmissionRiskModel(),
            'deterioration': PatientDeteriorationModel(),
            'mortality': MortalityRiskModel()
        }
        self.early_warning_system = EarlyWarningSystem()

    def assess_patient_risk(self, patient_data, risk_type='comprehensive'):
        """
        Umfassende Patientenrisikobewertung
        """
        if risk_type == 'sepsis':
            return self.assess_sepsis_risk(patient_data)
        elif risk_type == 'readmission':
            return self.assess_readmission_risk(patient_data)
        elif risk_type == 'comprehensive':
            return self.comprehensive_risk_assessment(patient_data)

    def assess_sepsis_risk(self, patient_data):
        """
        KI-basierte Sepsis-Früherkennung
        """
        # Vitaldaten analysieren
        vital_signs = patient_data['vital_signs']
        lab_values = patient_data['lab_values']
        clinical_notes = patient_data.get('clinical_notes', '')

        # SOFA Score mit KI-Enhancement
        sofa_components = {
            'respiratory': self.calculate_respiratory_score(vital_signs, lab_values),
            'cardiovascular': self.calculate_cardiovascular_score(vital_signs, lab_values),
            'hepatic': self.calculate_hepatic_score(lab_values),
            'coagulation': self.calculate_coagulation_score(lab_values),
            'renal': self.calculate_renal_score(lab_values),
            'neurological': self.calculate_neurological_score(patient_data)
        }

        traditional_sofa = sum(sofa_components.values())

        # KI-Enhanced Risk Score
        ai_features = self.extract_ai_features(patient_data)
        ai_risk_score = self.risk_models['sepsis'].predict_risk(ai_features)

        # Kombinierte Bewertung
        combined_risk = {
            'traditional_sofa_score': traditional_sofa,
            'ai_enhanced_score': ai_risk_score,
            'combined_risk_level': self.categorize_risk_level(traditional_sofa, ai_risk_score),
            'confidence': self.calculate_prediction_confidence(ai_features),
            'contributing_factors': self.identify_risk_factors(ai_features),
            'recommended_actions': self.generate_clinical_actions(traditional_sofa, ai_risk_score)
        }

        # Early Warning System Integration
        if combined_risk['combined_risk_level'] in ['high', 'critical']:
            alert = self.early_warning_system.trigger_sepsis_alert(
                patient_data['patient_id'],
                combined_risk
            )
            combined_risk['alert_triggered'] = alert

        return combined_risk

    def predict_hospital_readmission(self, patient_data, discharge_plan):
        """
        Vorhersage von Wiederaufnahme-Risiko
        """
        readmission_factors = {
            'demographic': self.analyze_demographic_factors(patient_data),
            'clinical': self.analyze_clinical_factors(patient_data),
            'social': self.analyze_social_determinants(patient_data),
            'discharge_planning': self.analyze_discharge_factors(discharge_plan),
            'medication_complexity': self.assess_medication_complexity(patient_data)
        }

        # 30-Tage Readmission Risk
        risk_prediction = self.risk_models['readmission'].predict_30_day_risk(readmission_factors)

        # Intervention Recommendations
        interventions = self.recommend_interventions(risk_prediction, readmission_factors)

        return {
            'readmission_probability': risk_prediction['probability'],
            'risk_category': risk_prediction['category'],
            'key_risk_factors': readmission_factors,
            'recommended_interventions': interventions,
            'estimated_cost_impact': self.calculate_cost_impact(risk_prediction),
            'follow_up_schedule': self.generate_follow_up_plan(risk_prediction)
        }

# Personalisierte Therapieplanung mit KI
class PersonalizedMedicineAI:
    def __init__(self):
        self.genomics_analyzer = GenomicsAnalyzer()
        self.drug_interaction_checker = DrugInteractionAI()
        self.treatment_optimizer = TreatmentOptimizer()

    def create_personalized_treatment_plan(self, patient_profile, condition):
        """
        Erstellung personalisierter Therapiepläne
        """
        # Genetische Faktoren analysieren
        if patient_profile.get('genomic_data'):
            pharmacogenomics = self.genomics_analyzer.analyze_drug_metabolism(
                patient_profile['genomic_data']
            )
        else:
            pharmacogenomics = None

        # Patientenspezifische Faktoren
        patient_factors = {
            'age': patient_profile['age'],
            'weight': patient_profile['weight'],
            'comorbidities': patient_profile['medical_history'],
            'current_medications': patient_profile['current_medications'],
            'allergies': patient_profile['allergies'],
            'kidney_function': patient_profile.get('creatinine_clearance'),
            'liver_function': patient_profile.get('liver_enzymes')
        }

        # Evidenzbasierte Therapieoptionen
        treatment_options = self.treatment_optimizer.get_evidence_based_options(
            condition, patient_factors
        )

        # Personalisierte Dosierung
        for option in treatment_options:
            option['personalized_dosing'] = self.calculate_personalized_dose(
                option['medication'],
                patient_factors,
                pharmacogenomics
            )

            option['interaction_risk'] = self.drug_interaction_checker.assess_interactions(
                option['medication'],
                patient_profile['current_medications']
            )

            option['side_effect_probability'] = self.predict_side_effects(
                option['medication'],
                patient_factors,
                pharmacogenomics
            )

        # Therapieplan-Ranking
        ranked_options = self.rank_treatment_options(treatment_options, patient_factors)

        optimal_plan = {
            'primary_treatment': ranked_options[0],
            'alternative_treatments': ranked_options[1:3],
            'monitoring_plan': self.create_monitoring_plan(ranked_options[0], patient_factors),
            'expected_outcomes': self.predict_treatment_outcomes(ranked_options[0], patient_factors),
            'contraindications': self.check_contraindications(ranked_options[0], patient_factors)
        }

        return optimal_plan

    def optimize_cancer_treatment(self, patient_data, tumor_profile):
        """
        Präzisionsonkologie mit KI
        """
        # Tumor-Genomik analysieren
        genomic_alterations = self.genomics_analyzer.analyze_tumor_mutations(
            tumor_profile['genomic_data']
        )

        # Targeted Therapy Matching
        targeted_therapies = self.match_targeted_therapies(
            genomic_alterations,
            tumor_profile['cancer_type']
        )

        # Immuntherapie-Eignung
        immunotherapy_prediction = self.predict_immunotherapy_response(
            tumor_profile,
            patient_data['immune_markers']
        )

        # Chemotherapie-Optimierung
        chemotherapy_optimization = self.optimize_chemotherapy_regimen(
            patient_data,
            tumor_profile
        )

        treatment_strategy = {
            'precision_therapies': targeted_therapies,
            'immunotherapy_options': immunotherapy_prediction,
            'chemotherapy_regimen': chemotherapy_optimization,
            'combination_strategies': self.identify_combination_opportunities(
                targeted_therapies, immunotherapy_prediction, chemotherapy_optimization
            ),
            'biomarker_monitoring': self.create_biomarker_monitoring_plan(genomic_alterations),
            'resistance_management': self.plan_resistance_monitoring(genomic_alterations)
        }

        return treatment_strategy

# DSGVO-konformer Healthcare Chatbot
class HealthcareChatbot:
    def __init__(self):
        self.medical_nlp = MedicalNLPEngine()
        self.symptom_checker = SymptomCheckerAI()
        self.appointment_scheduler = AppointmentScheduler()
        self.privacy_manager = HealthcarePrivacyManager()

    def handle_patient_inquiry(self, user_input, patient_context):
        """
        DSGVO-konforme Patientenanfragen
        """
        # Datenschutz-Einverständnis prüfen
        consent_check = self.privacy_manager.verify_consent(patient_context)
        if not consent_check['valid']:
            return self.request_consent(patient_context)

        # Intent Classification
        intent = self.medical_nlp.classify_medical_intent(user_input)

        if intent == 'symptom_inquiry':
            return self.handle_symptom_inquiry(user_input, patient_context)
        elif intent == 'appointment_booking':
            return self.handle_appointment_booking(user_input, patient_context)
        elif intent == 'medication_question':
            return self.handle_medication_inquiry(user_input, patient_context)
        elif intent == 'test_results':
            return self.handle_test_results_inquiry(user_input, patient_context)
        else:
            return self.escalate_to_healthcare_professional(user_input, patient_context)

    def handle_symptom_inquiry(self, symptoms_description, patient_context):
        """
        KI-gestützte Symptomberatung (KEINE Diagnose!)
        """
        # Wichtiger Disclaimer
        disclaimer = """
        ⚠️ WICHTIGER HINWEIS: Diese KI-Beratung ersetzt NICHT den Arztbesuch!
        Bei akuten Beschwerden oder Notfällen wenden Sie sich sofort an:
        - Notruf: 112
        - Ärztlicher Bereitschaftsdienst: 116 117
        """

        # Symptome extrahieren und kategorisieren
        extracted_symptoms = self.medical_nlp.extract_symptoms(symptoms_description)

        # Risikobewertung
        urgency_assessment = self.symptom_checker.assess_urgency(
            extracted_symptoms,
            patient_context.get('medical_history', [])
        )

        if urgency_assessment['urgency_level'] == 'emergency':
            return {
                'response': f"{disclaimer}\n\n🚨 NOTFALL ERKANNT!\nBitte rufen Sie SOFORT den Notruf 112!",
                'urgency': 'emergency',
                'immediate_action': 'call_emergency'
            }

        # Informative Antwort generieren
        information_response = self.generate_symptom_information(
            extracted_symptoms,
            urgency_assessment
        )

        # Empfehlungen
        recommendations = self.generate_health_recommendations(
            extracted_symptoms,
            urgency_assessment,
            patient_context
        )

        return {
            'response': f"{disclaimer}\n\n{information_response}\n\nEmpfehlungen:\n{recommendations}",
            'urgency': urgency_assessment['urgency_level'],
            'recommended_action': urgency_assessment['recommended_action'],
            'should_see_doctor': urgency_assessment['urgency_level'] in ['high', 'medium'],
            'information_only': True
        }

    def handle_appointment_booking(self, booking_request, patient_context):
        """
        Intelligente Terminbuchung
        """
        # Termindetails extrahieren
        appointment_details = self.medical_nlp.extract_appointment_details(booking_request)

        # Verfügbare Termine finden
        available_slots = self.appointment_scheduler.find_available_appointments(
            specialty=appointment_details.get('specialty'),
            urgency=appointment_details.get('urgency', 'normal'),
            patient_preferences=patient_context.get('preferences', {}),
            insurance_type=patient_context.get('insurance_type')
        )

        if available_slots:
            # Top 3 Optionen präsentieren
            top_options = available_slots[:3]

            options_text = "\n".join([
                f"• {slot['date']} um {slot['time']} - Dr. {slot['doctor']} ({slot['specialty']})"
                for slot in top_options
            ])

            response = f"""
            Ich habe passende Termine gefunden:

            {options_text}

            Welcher Termin passt Ihnen am besten?
            Antworten Sie einfach mit der Nummer (1, 2 oder 3).
            """

            return {
                'response': response,
                'available_appointments': top_options,
                'booking_context': appointment_details,
                'next_step': 'appointment_selection'
            }
        else:
            return {
                'response': """
                Leider sind keine Termine in Ihrem gewünschten Zeitraum verfügbar.

                Alternativen:
                • Ich kann Sie auf die Warteliste setzen
                • Telemedizin-Termin verfügbar
                • Andere Praxis in der Nähe vorschlagen

                Was würden Sie bevorzugen?
                """,
                'no_appointments': True,
                'alternatives': ['waitlist', 'telemedicine', 'other_practice']
            }

Healthcare_AI_ROI:
  Diagnostik_Effizienz:
    Radiologie_Befundung: '30-50% Zeitersparnis'
    Pathologie_Screening: '60-80% Zeitersparnis'
    Laborwert_Interpretation: '70-90% Zeitersparnis'
    Kostenersparnis_pro_Fall: '€50-200'

  Verwaltungsautomatisierung:
    Terminbuchung: '90% Automatisierung'
    Patientenaufnahme: '80% Automatisierung'
    Abrechnungsprozesse: '85% Automatisierung'
    Personalkosten_Einsparung: '€100.000-300.000 jährlich'

  Klinische_Effizienz:
    Früherkennung_Komplikationen: '40-60% weniger Notfälle'
    Verkürzte_Verweildauer: '1-3 Tage durchschnittlich'
    Reduzierte_Readmissions: '20-30% weniger'
    Kosteneinsparung_pro_Patient: '€500-2.000'

  Qualitätsverbesserung:
    Diagnostische_Genauigkeit: '+15-25%'
    Behandlungserfolg: '+20-35%'
    Patientenzufriedenheit: '+25-40%'
    Haftungsrisiko_Reduktion: '-30-50%'

  Investitionskosten:
    Initial_Setup: '€50.000-200.000'
    Jährliche_Lizenzkosten: '€20.000-80.000'
    Training_und_Integration: '€30.000-100.000'
    ROI_Zeitraum: '12-24 Monate'

# DSGVO-konforme Healthcare AI Implementation
class HealthcareAICompliance:
    def __init__(self):
        self.gdpr_manager = GDPRComplianceManager()
        self.medical_device_compliance = MedicalDeviceCompliance()
        self.audit_system = HealthcareAuditSystem()

    def implement_compliant_ai_system(self, system_config):
        """
        Implementierung rechtskonformer Healthcare AI
        """
        compliance_framework = {
            'data_protection': self.implement_data_protection(system_config),
            'medical_device_classification': self.classify_medical_device(system_config),
            'quality_management': self.implement_qms(system_config),
            'risk_management': self.implement_risk_management(system_config),
            'audit_trails': self.setup_audit_system(system_config)
        }

        return compliance_framework

    def implement_data_protection(self, system_config):
        """
        DSGVO-konforme Datenverarbeitung
        """
        data_protection_measures = {
            'data_minimization': {
                'principle': 'Nur medizinisch notwendige Daten verarbeiten',
                'implementation': 'Automatische Datenklassifikation und -filterung',
                'retention_policy': 'Automatische Löschung nach gesetzlichen Vorgaben'
            },
            'consent_management': {
                'patient_consent': 'Granulare Einverständniserklärungen',
                'withdrawal_mechanism': 'Einfache Widerrufsmöglichkeit',
                'documentation': 'Vollständige Consent-Dokumentation'
            },
            'technical_measures': {
                'encryption': 'Ende-zu-Ende-Verschlüsselung',
                'access_control': 'Rollenbasierte Zugriffskontrolle',
                'pseudonymization': 'Automatische Pseudonymisierung von Patientendaten'
            },
            'organizational_measures': {
                'staff_training': 'Regelmäßige Datenschutz-Schulungen',
                'data_protection_officer': 'Dedicated Healthcare DPO',
                'incident_response': 'Datenschutz-Incident-Response-Plan'
            }
        }

        return data_protection_measures

    def classify_medical_device(self, ai_system_spec):
        """
        Medizinprodukte-Klassifikation für KI-Systeme
        """
        risk_assessment = {
            'intended_use': ai_system_spec['clinical_purpose'],
            'patient_risk': self.assess_patient_risk(ai_system_spec),
            'diagnostic_impact': self.assess_diagnostic_impact(ai_system_spec),
            'treatment_influence': self.assess_treatment_influence(ai_system_spec)
        }

        # EU MDR Klassifikation
        if risk_assessment['patient_risk'] == 'high':
            device_class = 'Class III'
            regulatory_pathway = 'Notified Body Assessment'
        elif risk_assessment['diagnostic_impact'] == 'high':
            device_class = 'Class IIb'
            regulatory_pathway = 'Notified Body Assessment'
        elif risk_assessment['treatment_influence'] == 'medium':
            device_class = 'Class IIa'
            regulatory_pathway = 'Conformity Assessment'
        else:
            device_class = 'Class I'
            regulatory_pathway = 'Self-Declaration'

        return {
            'device_classification': device_class,
            'regulatory_pathway': regulatory_pathway,
            'required_documentation': self.get_required_documentation(device_class),
            'clinical_evaluation_needed': device_class in ['Class IIa', 'Class IIb', 'Class III'],
            'post_market_surveillance': self.define_surveillance_plan(device_class)
        }