Discover Which Laws and Frameworks Touch AI in Healthcare
As artificial intelligence transforms how healthcare is delivered, from diagnostics to patient monitoring, a growing body of global laws is emerging to govern its use. These regulations are not optional. If your AI system processes medical data, assists in clinical decisions, or interacts directly with patients, it operates in a highly regulated environment.
Below is a clear breakdown of the key laws and frameworks influencing AI in healthcare. You’ll see what each covers, how strict it is, and why it matters for anyone building or deploying AI in this space.
HIPAA (USA)
Defending your fundamental rights, we fight against discrimination and injustice, ensuring that your civil liberties are upheld in every situation.
GDPR (EU)
Navigating the complexities of insurance claims, we provide expert legal guidance to ensure you receive the coverage and benefits you’re entitled to.
PHIPA
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PIPEDA (Canada)
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EU AI Act (EU)
We hold healthcare professionals accountable for their actions, striving to obtain justice for those harmed by medical errors.
NIST AI RMF
Advocating for victims of negligence, our personal injury team fights to secure the compensation you deserve for your pain, suffering, and losses.
FDA AI/ML
Defending your fundamental rights, we fight against discrimination and injustice, ensuring that your civil liberties are upheld in every situation.
SOC 2
Navigating the complexities of insurance claims, we provide expert legal guidance to ensure you receive the coverage and benefits you’re entitled to.
ISO/IEC 42001
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ISO/IEC 27001
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OECD AI Principles
We hold healthcare professionals accountable for their actions, striving to obtain justice for those harmed by medical errors.
Existing Laws vs. AI-Specific Regulations
When navigating AI compliance in healthcare, it’s essential to distinguish between long-standing data privacy and security frameworks—like HIPAA, GDPR, or ISO 27001—and the newer generation of laws and standards designed specifically for artificial intelligence. While both are important, they serve different purposes: general frameworks protect sensitive data and ensure organizational accountability, whereas AI-specific regulations address emerging challenges like algorithmic risk, explainability, and system transparency. Understanding this distinction is key to building trustworthy, compliant AI systems in healthcare.
General Frameworks & Laws
These were originally developed for data privacy, cybersecurity, and operational controls—not AI directly. However, they still apply to AI systems when those systems handle personal health data, impact patient care, or integrate into clinical workflows.
| Type | Example | Applies to AI How? |
|---|---|---|
| Privacy Law | HIPAA (USA) | Governs how AI systems must protect PHI (e.g. speech-to-text in clinical tools) |
| Privacy Law | GDPR (EU) | Requires lawful processing, consent, and rights for automated decision-making |
| Security Standard | ISO/IEC 27001 | Provides a security framework for AI infrastructure and data handling |
| Audit/Trust Framework | SOC 2 | Ensures cloud-based AI vendors meet trust criteria (security, availability, etc.) |
| Healthcare-Specific Privacy Law | PHIPA (Ontario) | Regulates use of patient data by clinics adopting AI systems in Canada |
| General Privacy Law | PIPEDA (Canada) | Applies to AI vendors processing personal data in commercial contexts |
AI-Specific Regulations & Frameworks
These are explicitly designed to govern how artificial intelligence is developed, deployed, and monitored—especially in high-risk sectors like healthcare.
| Type | Example | What’s New? |
|---|---|---|
| Risk-Based AI Law | EU AI Act | Categorizes AI systems by risk (e.g., diagnostic tools = high risk) and imposes strict controls |
| AI Governance Standard | ISO/IEC 42001 | First global standard for AI Management Systems (AIMS) — focuses on governance, risk, transparency |
| Regulatory Guidance | FDA AI/ML SaMD | Offers a lifecycle-based approach to AI/ML used in medical devices (US-specific) |
| Risk Management Framework | NIST AI RMF | U.S. voluntary framework to help developers manage AI risk (governance, accountability, fairness) |
| Ethical Guidelines | OECD AI Principles | Non-binding but globally endorsed — promotes transparency, accountability, and human-centered AI |