Clinical AI Audit
Audit AI-patient conversations and clinical SOAP notes for hallucinations, safety violations, and compliance issues. HIPAA-compliant. Zero data retention.
HIPAA Compliant
HIPAA BAA-covered infrastructure
Zero Data Retention
No patient data stored or logged
Multi-Model Consensus
Independent verification across AI systems
Physician-Scientist Built
Actively practicing cardiologist, 150+ publications, ML training at Georgia Tech
Comprehensive Safety Flags
Dozens of clinically specific detection categories
Real-Time Streaming
Watch audit progress in real time
Audit Capabilities
Every submission goes through rapid screening, deep clinical analysis, and independent multi-model cross-verification.
AI Conversation Audit
Audit AI-patient conversation transcripts for prompt injection, clinical hallucinations, fabricated medical authorities, scope violations, and safety threats. Every conversation is screened, analyzed, and cross-validated by multiple independent AI systems.
Try it→Clinical Note Audit
Audit SOAP notes and clinical documentation generated by AI systems for dosage safety errors, documentation integrity failures, transcript infidelity, security violations, and compliance issues. Automated SOAP note audit catches errors before they enter the medical record.
Try it→Enterprise Batch ProcessingEnterprise
Upload CSV files with hundreds of records for automated batch auditing of AI-patient conversations and clinical notes. REST API integration for CI/CD pipelines, structured JSON results, and audit trail documentation.
Learn more→How It Works
Submit
Upload a conversation transcript, clinical note, or CSV batch through the web interface or REST API.
Automated Audit
The system runs your content through a proprietary multi-stage pipeline. Multiple independent AI systems cross-validate findings so no single model determines the result.
Actionable Results
Receive a structured report with flagged issues, severity levels, confidence scores, and recommendations — streamed in real time or delivered as a batch report.
Built for Your Organization
Purpose-built healthcare AI quality assurance for the organizations that need it most.
For Digital Health Companies
Integrate audit into your CI/CD pipeline via REST API to catch hallucinations, safety violations, and scope issues before patients encounter them. Generate structured audit evidence for FDA submissions and demonstrate safety to regulators before market clearance.
- Pre-deployment validation via REST API
- CI/CD integration with structured JSON results
- FDA submission evidence generation
- Catch the 27% hallucination rate in healthcare AI before release
For Health Systems and Hospitals
Validate vendor AI chatbots and documentation tools before deploying to patients and clinicians. Ongoing monitoring catches performance drift. Audit trail documentation supports Joint Commission accreditation and CMS compliance requirements.
- Vendor AI validation before deployment
- Ongoing monitoring for performance drift
- Joint Commission and CMS audit trail documentation
- Reduce malpractice exposure from AI-assisted decisions
For CROs and Pharmaceutical Companies
Audit AI-generated clinical trial documentation and validate AI in endpoint adjudication workflows. Create regulatory-grade audit evidence that meets 21 CFR Part 11 requirements for electronic records and signatures.
- Clinical trial documentation audit
- Endpoint adjudication AI validation
- Regulatory-grade audit trails
- 21 CFR Part 11 compliant evidence
Why Healthcare AI Needs Purpose-Built Auditing
Generic AI governance platforms evaluate bias, fairness, and explainability across all industries. They were not designed to detect a healthcare chatbot fabricating a medical guideline, citing a non-existent clinical study, or providing treatment recommendations outside its authorized clinical scope.
Healthcare AI has unique failure modes that require domain-specific detection: clinical hallucinations where the AI generates plausible but incorrect medical information, fabricated citations to non-existent studies or guidelines, scope violations where a triage chatbot begins providing diagnostic or treatment advice, and documentation infidelity where AI-generated SOAP notes diverge from the source conversation.
Clinical AI Audit was designed by a physician-scientist who understands which errors are clinically dangerous versus cosmetically imperfect. It detects dozens of clinically specific flag types using multi-model consensus — because in clinical safety, false negatives are more dangerous than false positives.
The Regulatory Landscape Is Accelerating
Healthcare organizations deploying AI face an expanding web of federal, state, and accreditation requirements. The window for voluntary adoption of AI auditing is closing as requirements become mandatory.
Federal Requirements
- FDA — expanding clinical AI oversight under the 21st Century Cures Act, with Good Machine Learning Practice (GMLP) principles requiring ongoing monitoring of AI performance and safety
- ONC — HTI-1 transparency requirements mandate disclosure and validation of AI used in certified EHR systems
- CMS — Conditions of Participation increasingly reference AI governance for participating hospitals
State Laws
- California AB 3030 — requires disclosure when AI is used in clinical communications with patients
- Colorado AI Act — mandates impact assessments for high-risk AI systems, including healthcare applications
Accreditation Standards
- Joint Commission — developing AI safety standards in collaboration with the Coalition for Health AI (CHAI)
- NCQA — evaluating AI governance criteria for health plan accreditation and quality measurement
Organizations implementing AI auditing now build the compliance infrastructure they will need within 12–24 months.
Frequently Asked Questions
What is a healthcare AI audit?
A healthcare AI audit is a systematic evaluation of AI-generated clinical content — including patient conversations, chatbot responses, and clinical documentation — to detect safety violations, medical inaccuracies, prompt injection attacks, fabricated authorities, and compliance issues before they reach patients or become part of the medical record.
How does multi-model consensus reduce audit errors?
Multi-model consensus sends each audit to multiple independent AI systems that analyze the content separately. Findings confirmed by multiple models have higher confidence. This eliminates single-model blind spots and reduces both false positives and false negatives — critical in clinical safety where a missed flag can harm patients.
What types of AI safety violations can this system detect?
The system detects a comprehensive range of healthcare AI safety violations including prompt injection attacks, clinical hallucinations, fabricated medical authorities, scope violations, dosage safety errors, documentation integrity failures, transcript infidelity, unauthorized medical advice, and security threats — across dozens of clinically specific flag types designed by a physician-scientist.
Is the audit system HIPAA compliant?
Yes. The system processes all data under a HIPAA Business Associate Agreement with zero data retention — no patient information is stored or logged. All processing runs on HIPAA BAA-covered cloud infrastructure with enterprise-grade encryption, web application firewall protection, and zero data retention.
How does this differ from general AI governance tools?
General AI governance platforms evaluate bias, fairness, and explainability across all industries. They cannot detect a healthcare chatbot fabricating a medical guideline, citing a non-existent study, or providing advice outside its authorized scope. This system is purpose-built for healthcare with 53 clinically specific flags designed by a physician-scientist.
What AI models are used in the audit pipeline?
The pipeline uses multiple independent AI systems at different stages — rapid screening, deep clinical analysis, and cross-validation — so no single model determines the final audit result. All models run on HIPAA BAA-covered infrastructure with enterprise-grade security controls.
Can this integrate into our CI/CD pipeline?
Yes. The system exposes a REST API that accepts conversation transcripts and clinical notes, returning structured JSON audit results. Integrate it into CI/CD workflows to automatically audit AI-generated content before deployment, with batch processing for high-volume enterprise use cases.
What regulations require healthcare AI auditing?
FDA is expanding clinical AI oversight under the 21st Century Cures Act and GMLP principles. ONC HTI-1 requires transparency for AI in certified EHRs. California AB 3030 mandates AI disclosure in clinical communications. The Joint Commission is developing AI safety standards. These requirements create a compliance imperative for organizations deploying healthcare AI.
Ready to Audit Your Healthcare AI?
Try the audit system with sample data to see it in action, or request an enterprise demo for batch processing, API integration, and custom reporting.