Healthcare AI Compliance and Governance Consulting

We implement immutable logging systems that capture every model inference, data input, and user interaction. This creates a defensible audit trail for FDA SaMD submissions and internal compliance reviews, reducing validation time by up to 40%.

We engineer secure data ingestion and processing pipelines with encryption-in-transit/at-rest, strict access controls, and automated PHI detection. This ensures patient data privacy is baked into your AI system's core, not bolted on.

We deploy a centralized dashboard for continuous monitoring of model performance, fairness metrics, and data drift. This provides real-time visibility for your compliance officers and IT teams, enabling proactive risk management.

We conduct gap analyses and implement specific technical controls—from human oversight mechanisms for high-risk systems to comprehensive risk management documentation—ensuring your AI aligns with both EU MDR and the upcoming AI Act.

Using frameworks like NIST AI RMF, we perform rigorous fairness testing across protected classes. We deliver a detailed report with quantified bias metrics and implement technical mitigation strategies, such as re-weighting training data or post-processing adjustments.

We provide a living document with standard operating procedures (SOPs) for model updates, incident response, and change management. This turns governance from a theoretical framework into an executable process your team can follow, accelerating your path to a secure launch. For a deeper dive into model validation, see our guide on Clinical AI Model Validation and Auditing.

End-to-end validation and audit trail generation for FDA SaMD (Software as a Medical Device) submissions. We ensure your computer vision models for radiology meet 21 CFR Part 11 and IEC 62304 standards for design controls and software lifecycle management.

Implementation of NIST AI RMF-aligned governance for patient risk models (e.g., readmission, sepsis). Includes algorithmic bias auditing, continuous performance monitoring dashboards, and documentation for health equity reporting.

Architecting real-time speech-to-text and NLP pipelines with built-in PHI redaction, consent management, and audit logs to satisfy HIPAA Privacy and Security Rules for automated clinical note generation.

Technical auditing and remediation of AI-driven CDSS integrated into EHRs to ensure alignment with evidence-based medicine, mitigate clinical liability, and comply with EU MDR requirements for clinical evaluation.

Engineering privacy-preserving, decentralized training networks that enable cross-institutional AI development without centralizing PHI, ensuring compliance with data sovereignty laws and institutional review board (IRB) protocols.

Creation of high-fidelity, statistically representative synthetic patient datasets using differential privacy techniques. Enables AI training and testing without real PHI, solving data scarcity while maintaining HIPAA compliance and supporting regulatory submissions.