To ensure we provide the most robust and actionable compliance intelligence for the healthcare AI sector, we are transitioning from weekly to monthly updates. This allows us to focus on high-impact regulatory shifts and provide the depth of analysis your business requires. The monthly updates will be delivered on first Monday of the subsequent month.
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Canada
1) Health Canada Modernization Plan (January 7, 2026)
Health Canada’s 2025-26 Departmental Plan identifies digital tool modernization as a primary pillar. The plan specifically supports the Pan-Canadian Interoperability Roadmap to enable secure access to health information for patients and providers through ongoing investments in Canada Health Infoway.
Health Canada 2025-26 Departmental Plan
How it applies to AI in Healthcare: Modernizing data infrastructure and supporting interoperability are foundational requirements for deploying scalable AI solutions that require secure, high-quality patient data access across jurisdictions.
2) Mandatory Electronic Regulatory Enrolment (REP) (January 13, 2026)
Health Canada has transitioned to a mandatory Regulatory Enrolment Process (REP) for Class II, III, and IV medical device submissions. These must now be filed through the Common Electronic Submission Gateway (CESG); manual email or non-standard submissions are no longer accepted.
Regulatory enrolment process (REP) – Canada.ca
How it applies to AI in Healthcare: AI-based medical devices falling under Class II-IV must comply with this digital-only submission workflow, streamlining the regulatory interface for AI developers but requiring strict adherence to the CESG channel.
3) High-Impact AI Classification (January 9, 2026)
Under Bill C-27’s Artificial Intelligence and Data Act (AIDA), systems designated as “high-impact” face enhanced obligations for risk mitigation and incident reporting. Clinical decision support and diagnostics are identified as strong candidates for this classification.
The Artificial Intelligence and Data Act (AIDA) – Companion document
How it applies to AI in Healthcare: Healthcare AI developers should design systems with high-impact governance in mind, focusing on auditability and risk controls for any tool influencing clinical outcomes.
4) Automated Decision-Making Systems (ADMS) Compliance (January 12, 2026)
The Treasury Board Secretariat (TBS) continues to enforce the Directive on Automated Decision-Making. Federal institutions must complete Algorithmic Impact Assessments (AIA) for systems used in resource prioritization or health-related benefits eligibility.
Algorithmic Impact Assessment tool – Canada.ca
How it applies to AI in Healthcare: Vendors selling AI solutions to federal healthcare programs must ensure their tools are ready for AIA-driven due diligence regarding transparency and human oversight.
5) AI Governance Roles (January 16, 2026)
Policy materials emphasize shared accountability for AI governance among CIOs and CDOs within federal institutions. These roles oversee data quality and risk management for AI systems used in sensitive domains like public health analytics.
Guide to Departmental AI Responsibilities
How it applies to AI in Healthcare: Organizations should align their internal governance with these federal structures, particularly regarding data provenance and bias controls in health analytics.
Rest of the World
1) UN-Affiliated AI Healthcare Governance Work (January 1, 2026)
UN-affiliated bodies have released guidance on AI governance in high-risk sectors. While non-binding, these frameworks promote end-to-end lifecycle governance covering data collection and real-world clinical impact.
White Paper on AI Healthcare Governance – UNPAN
How it applies to AI in Healthcare: International operators should track these principles as they often inform future national regulations and global procurement criteria.
2) FDA–EMA Joint AI Principles (January 14, 2026)
The FDA and EMA have established joint principles for Good Machine Learning Practice. While focused on drug development, the standards for transparency and human oversight are becoming benchmarks for all regulated healthcare AI.
EMA and FDA set common principles for AI in medicine development
How it applies to AI in Healthcare: Aligning clinical AI governance with these joint principles strengthens regulatory readiness and audit defensibility across both North American and European markets.
Cross-Cutting Themes
- The Death of “Black Box” AI: Transparency is now a legal requirement. Whether it’s AIDA in Canada or FDA/EMA principles globally, you must be able to explain how your model reached a decision, especially in clinical settings.
- Lifecycle Liability: Regulation no longer ends at “Market Approval.” You are now responsible for “Model Drift” and “Post-Market Vigilance.” Continuous monitoring is a core legal obligation.
- Interoperability as a Barrier to Entry: Data silos are being legislated out of existence. Interoperability is moving from a “feature” to a “compliance requirement.”
Immediate, concrete checklist for health organisations & vendors
For Founders & Business Owners
- [ ] Review Product Roadmap for S-5 Compliance: Ensure your engineering team is building toward the Pan-Canadian Interoperability Roadmap standards.
- [ ] Budget for Compliance Audits: Allocate 15-20% of R&D cycles to documentation, risk mitigation, and mandatory reporting required for “High-Impact” systems.
- [ ] Insurance Review: Update your professional liability insurance to specifically cover “Algorithmic Malpractice” and “Data Bias” claims under the new AIDA framework.
For Compliance & Regulatory Specialists
- [ ] CESG Account Verification: Ensure your organization has an active, tested account on the Common Electronic Submission Gateway (CESG). And verify that all current and pending Class II, III, and IV medical devices application are formatted for the Mandatory Electronic Regulatory Enrolment (REP) via the CESG.
- [ ] Conduct an Internal AIA: Use the TBS Algorithmic Impact Assessment tool on your own products now to identify gaps in transparency or bias before a client (or regulator) asks for it.
- [ ] Implement “Model Drift” Logging: Establish a formal process for tracking performance changes post-deployment. This is required for “significant change” reporting under the new REP rules.
- [ ] Review Consent Protocols for AI Scribes: If using transcription tools, ensure your consent forms explicitly mention AI processing, storage duration, and the “Human-in-the-loop” review process.
Disclaimer: This checklist is provided for general informational purposes only and does not constitute legal, regulatory, or professional advice; organizations should consult with their legal and compliance departments to ensure adherence to specific jurisdictional requirements.
Sources
- Health Canada Modernization Plan (January 7, 2026)
https://www.canada.ca/en/health-canada/corporate/transparency/corporate-management-reporting/report-plans-priorities/2025-2026-departmental-plan.html - Mandatory Electronic Regulatory Enrolment (REP) (January 13, 2026)
https://www.canada.ca/en/health-canada/services/drugs-health-products/drug-products/applications-submissions/guidance-documents/regulatory-enrolment-process.html - High-Impact AI Classification (Bill C-27 / AIDA) (January 9, 2026)
https://ised-isde.canada.ca/site/innovation-better-canada/en/artificial-intelligence-and-data-act-aida-companion-document - Automated Decision-Making Systems (ADMS) Compliance (TBS) (January 12, 2026)
https://www.tbs-sct.canada.ca/pol/doc-eng.aspx?id=32592 - AI Governance Roles (Treasury Board) (January 16, 2026)
https://www.canada.ca/en/public-service-commission/services/appointment-framework/guides-tools-appointment-framework/ai-hiring-process.html - UN-Affiliated AI Healthcare Governance Work (January 1, 2026)
https://unpan.un.org/resources/content-type/Publication - FDA–EMA Joint AI Principles (Scope Clarification) (January 14, 2026)
https://www.ema.europa.eu/en/news/ema-fda-set-common-principles-ai-medicine-development-0
