The Challenge
AI in Healthcare: Rapid Adoption, Critical Stakes
Healthcare AI adoption has reached an inflection point. A 2025 AMA survey found that 66% of physicians are already using health-AI tools—up from 38% in 2023—and 68% believe AI positively contributes to patient care. The FDA has authorized over 1,250 AI-enabled medical devices as of July 2025, with nearly 400 AI algorithms approved for radiology alone.
The stakes are profound: AI systems influence diagnoses, treatment recommendations, patient monitoring, and resource allocation. Unlike e-commerce or marketing applications, healthcare AI failures can result in patient harm, regulatory action, and litigation.
Yet governance infrastructure has not kept pace with deployment velocity. Organizations are implementing AI systems that process protected health information, influence clinical decisions, and operate in regulated product contexts—often with governance frameworks designed for traditional software.
Why Traditional Compliance Fails for Healthcare AI
Three Critical Gaps:
Traditional HIPAA compliance relies on periodic risk assessments, annual reviews, and point-in-time audits. But AI systems process thousands of patient records per day, making millions of decisions between audits.
Existing compliance frameworks prove controls exist—they don't prove controls were applied to every transaction. When a regulator asks "did this specific AI decision follow your stated policies?", documentation-based compliance can't answer definitively.
FDA's Predetermined Change Control Plans (PCCPs) enable AI models to learn and update post-market. But continuous learning requires continuous governance.
"Healthcare entities using AI for clinical decision support must incorporate these systems into their risk analysis and management processes."
— HHS Proposed HIPAA Security Rule Update, 2025
Regulatory Landscape
HIPAA: Privacy and Security for AI Systems
What it is: The Health Insurance Portability and Accountability Act establishes national standards for protecting electronic protected health information (ePHI).
AI-Specific Provisions:
- AI systems processing ePHI must implement administrative, physical, and technical safeguards
- The 2025 proposed Security Rule update explicitly requires AI tools in risk analysis
- Minimum necessary standard applies to AI data access
- Business Associate Agreements required for AI vendors processing PHI
FDA AI/ML Medical Device Guidance
What it is: FDA regulates AI systems that meet the definition of a medical device, including clinical decision support tools and diagnostic AI.
AI-Specific Provisions:
- Predetermined Change Control Plans (PCCPs) for continuous learning AI
- January 2025 draft guidance on AI-enabled device lifecycle management
- Quality management system requirements
- Real-world performance monitoring
Section 1557 Anti-Discrimination Requirements
Section 1557 of the Affordable Care Act prohibits discrimination in healthcare, with specific AI provisions added in 2024. Healthcare providers using clinical decision support tools must ensure AI doesn't discriminate.
Status: AI provisions effective as of May 1, 2025
State Healthcare AI Laws
| State | Law | Key Requirement | Effective Date |
|---|---|---|---|
| Texas | HB 1709 | Written disclosure to patients when AI used in care | January 1, 2026 |
| Illinois | Therapy AI Law | Prohibits AI from making independent therapeutic decisions | August 1, 2025 |
| Colorado | AI Act | Impact assessments for high-risk healthcare AI | 2026 |
| Washington | MHMDA | Consumer health data protections (non-HIPAA) | In effect |
Use Cases
Clinical Decision Support Systems
AI systems that analyze patient data to assist clinicians in making treatment decisions, identifying risks, or recommending interventions. Includes sepsis prediction, medication dosing, and treatment pathway recommendations.
Compliance Requirements:
- HIPAA compliance for all PHI processing
- FDA oversight if meets medical device definition
- Section 1557 non-discrimination requirements
- Audit trails for clinical decisions
How GLACIS Addresses This:
- Runtime policy enforcement: Validates each inference against clinical safety policies before output reaches clinicians
- Per-inference attestation: Creates cryptographic proof that every recommendation followed approved guidelines
- Drift detection: Alerts when model behavior deviates from validated baselines
Diagnostic Imaging AI
AI systems that analyze medical images (X-rays, CT scans, MRIs) to detect abnormalities, prioritize worklists, or assist in diagnosis. Includes lung nodule detection, mammography analysis, and diabetic retinopathy screening.
Compliance Requirements:
- FDA clearance/approval as medical device (510(k), De Novo, or PMA)
- Quality Management System (QMS) requirements
- Post-market surveillance and reporting
- HIPAA for PHI in images
How GLACIS Addresses This:
- Continuous monitoring: Tracks model performance against validated accuracy thresholds
- Policy enforcement: Ensures only authorized model versions process patient images
- Evidence generation: Creates documentation package for FDA post-market requirements
AI-Powered Patient Monitoring
AI systems that monitor patient vital signs, predict deterioration, or alert care teams to changing conditions. Includes ICU monitoring, remote patient monitoring via wearables, and early warning systems.
Compliance Requirements:
- FDA oversight for clinical alarm systems
- HIPAA for remote monitoring data
- State telehealth regulations
- Clinical workflow integration requirements
How GLACIS Addresses This:
- Real-time enforcement: Validates monitoring alerts against clinical thresholds before delivery
- Subgroup performance: Tracks model performance across patient demographics
- Latency guarantees: Sub-50ms overhead ensures alerts aren't delayed
Evidence & Attestation
What Healthcare Buyers Require
- Security questionnaires: SOC 2, HITRUST CSF, ISO 27001 evidence
- HIPAA compliance: Business Associate Agreement, risk assessment documentation
- AI-specific evidence: Model cards, bias assessments, validation reports
- Audit trails: Logs of AI access to PHI, decision records
- Incident response: AI-specific breach notification and response plans
GLACIS Evidence Types
| Evidence Type | Description | Regulatory Mapping |
|---|---|---|
| Per-inference attestation | Cryptographic proof each inference followed policy | HIPAA audit controls, FDA QMS |
| Policy enforcement logs | Complete record of policies applied to each transaction | HIPAA access controls |
| Performance monitoring | Continuous accuracy and drift tracking | FDA post-market surveillance |
| Access control records | Log of data accessed for each inference | HIPAA minimum necessary |