Healthcare platforms require specialized image moderation that understands medical context. Our HIPAA-compliant AI distinguishes clinical imagery from inappropriate content while protecting patient privacy through PHI detection and redaction.
Medical images present unique moderation challenges. Clinical photographs, X-rays, surgical images, and dermatological photos often show body parts and conditions that would trigger false positives in generic moderation systems. At the same time, these images must be protected from unauthorized access and PHI exposure.
Our medical image moderation is trained on healthcare imagery and understands the difference between clinical documentation and inappropriate content. It simultaneously scans for embedded PHI including patient names, dates of birth, medical record numbers, and other identifiers that require protection under HIPAA.
All processing is performed in HIPAA-compliant infrastructure with Business Associate Agreement (BAA) coverage, encryption in transit and at rest, and comprehensive audit logging.
Recognize clinical imagery including X-rays, CT scans, dermatological photos, surgical images, and wound documentation.
Identify and optionally redact patient names, DOB, MRN, and other protected health information in medical images.
Extract and analyze DICOM headers for PHI, ensuring complete metadata protection for medical imaging files.
Complete logging of all moderation decisions with timestamps, confidence scores, and reasoning for audit requirements.
Detect non-medical inappropriate content that may be uploaded to healthcare platforms while preserving legitimate clinical imagery.
Images are processed in memory and immediately discarded. No medical images are stored on our servers.
Screen patient-uploaded photos for appropriateness while allowing legitimate medical imagery for virtual consultations.
Moderate images shared through patient messaging systems and health record uploads.
Ensure educational medical imagery is properly de-identified before use in training materials.
Moderate physician communities and medical case discussion platforms for appropriate sharing.
Screen participant-uploaded photos and clinical imagery while ensuring PHI protection.
Moderate images captured through connected medical devices and health monitoring apps.
Integrate medical image moderation with full compliance assurance. All connections are encrypted and BAA-covered.
# Python - Medical image moderation with PHI detection import requests def moderate_medical_image(image_data, api_key): response = requests.post( "https://api.imagemoderationapi.com/v1/healthcare/moderate", headers={ "Authorization": f"Bearer {api_key}", "X-HIPAA-Mode": "enabled" }, json={ "image_base64": image_data, "models": ["medical_context", "phi_detection", "nsfw"], "phi_config": { "redact_output": True, "scan_dicom": True, "audit_log": True } } ) result = response.json() # Check if PHI detected and return redacted version if result["phi"]["detected"]: return { "action": "redact", "redacted_image": result["redacted_base64"], "audit_id": result["audit_id"] } return {"action": "allow", "audit_id": result["audit_id"]}
Yes. We maintain full HIPAA compliance including BAA coverage, encryption, access controls, and audit logging. We're also SOC 2 Type II certified.
No. All images are processed in memory and immediately discarded. We maintain only metadata logs required for audit compliance, never image data.
We can process DICOM files, extracting and scanning both the image data and all DICOM metadata tags for PHI, including private tags that often contain patient information.
We detect patient names, dates of birth, MRNs, SSNs, addresses, phone numbers, and other identifiers in both image pixels (burned-in text) and embedded metadata.
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