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 continues the federal government’s multi-year commitment to digital modernization across the healthcare system. Interoperability remains a central policy objective, supported through collaboration with Canada Health Infoway and the Canadian Institute for Health Information. Federal digital health strategies emphasize the implementation of common data standards and improved cross-jurisdictional information exchange to enable “connected care.” These initiatives build on the Pan-Canadian Interoperability Roadmap and ongoing efforts to ensure that health information is securely accessible to providers and patients across provinces and territories.
Health Canada 2025-26 Departmental Plan
How it applies to AI in Healthcare: While the Departmental Plan does not directly regulate AI, it outlines the digital infrastructure upon which clinical AI systems depend. High-quality AI systems require access to standardized, interoperable, and longitudinal datasets to function safely and effectively. The emphasis on common standards—such as HL7 FHIR—signals that future AI tools deployed in clinical settings must integrate with evolving national data ecosystems. For students and developers, this underscores that interoperability is a foundational requirement for scalable AI adoption in healthcare.
2) Mandatory Electronic Regulatory Enrolment (REP) (January 13, 2026)
Health Canada continues to advance the Regulatory Enrolment Process (REP), which modernizes how regulated parties submit medical device applications. The use of the Common Electronic Submission Gateway (CESG) has become standard for many medical device submissions, particularly for higher-risk classes. The REP framework supports structured electronic submissions and enhances traceability across the device lifecycle. This modernization effort aligns Canada with broader international trends toward digital regulatory workflows and more transparent submission management systems.
Regulatory enrolment process (REP) – Canada.ca
How it applies to AI in Healthcare: For AI-enabled medical devices, this transition reinforces a “digital-first” compliance environment. Structured submissions improve regulatory traceability and lifecycle documentation, which is particularly relevant for software-based and AI-driven products that may undergo iterative updates. While existing regulations already require reporting of significant changes, the digital submission infrastructure facilitates clearer documentation of modifications over time. Developers of AI-based Software as a Medical Device (SaMD) should ensure that technical documentation is formatted and maintained in accordance with Health Canada’s electronic submission standards.
3) Automated Decision-Making Systems (ADMS) Compliance (January 12, 2026)
The Treasury Board of Canada Secretariat continues to enforce the Directive on Automated Decision-Making, which has governed the use of AI systems within federal institutions since 2019. The Directive requires departments to complete an Algorithmic Impact Assessment (AIA) before deploying systems that assist or replace human judgment in administrative decisions. The AIA framework evaluates risk levels and imposes corresponding transparency, oversight, and documentation obligations. These requirements are designed to ensure accountability, fairness, and procedural transparency in federal automated decision-making.
Algorithmic Impact Assessment tool – Canada.ca
How it applies to AI in Healthcare: AI tools used in federal healthcare-related programs—such as benefits administration or federally delivered health services—are subject to this Directive. Vendors supplying AI systems to federal institutions may be required to provide documentation regarding system performance, bias mitigation, explainability, and human oversight mechanisms. The Directive establishes a practical benchmark for responsible AI governance, illustrating that compliance increasingly involves assessing both technical functionality and socio-technical impacts.
4) AI Governance Roles (January 16, 2026)
Canada’s federal AI governance framework continues to operate under the Directive on Automated Decision-Making administered by the Treasury Board of Canada Secretariat. The Directive requires federal institutions to complete an Algorithmic Impact Assessment (AIA) before deploying systems that assist or replace human decision-making in administrative processes.
In parallel, federal digital policy emphasizes strengthened data governance, clear accountability roles for Chief Information Officers (CIOs) and Chief Data Officers (CDOs), and multidisciplinary oversight of AI initiatives. While these policies do not create a standalone AI statute, they embed AI oversight within existing digital governance, privacy, and administrative law frameworks.
Guide to Departmental AI Responsibilities
How it applies to AI in Healthcare:
For healthcare developers and clinicians, the domestic environment signals that AI oversight is embedded within broader digital governance structures.
- Operational Readiness:
AI tools procured or used by federal healthcare programs must undergo Algorithmic Impact Assessments. Vendors may be required to demonstrate transparency, documentation, and bias mitigation processes. - Interoperability as Practical Gatekeeper:
Although not yet a federally legislated requirement, alignment with interoperability standards such as HL7 FHIR is increasingly necessary for integration within provincial and federal digital health ecosystems. - Human Oversight & Fairness:
Federal policy requires meaningful human oversight and transparency proportional to system risk levels. Organizations deploying AI in public healthcare contexts must demonstrate procedural fairness and accountability.
Rest of the World
1) FDA–EMA Joint AI Principles (January 14, 2026)
Regulatory authorities, including the U.S. Food and Drug Administration and the European Medicines Agency, continue collaborative discussions regarding the responsible use of AI in medicine and drug development. These efforts build on existing guidance related to software-based medical technologies and good machine learning practices. While formal joint binding frameworks remain limited, regulators increasingly emphasize human oversight, clearly defined context of use, performance validation, and post-market monitoring for AI applications in healthcare and life sciences.
EMA and FDA set common principles for AI in medicine development
How it applies to AI in Healthcare: Although many initiatives focus specifically on drug development or regulated medical products, the underlying governance themes extend to broader healthcare AI applications. Best practices such as human-in-the-loop design, transparent validation processes, and continuous monitoring are becoming common expectations across jurisdictions. Organizations that proactively align with these principles strengthen their regulatory defensibility and readiness for future formalized standards.
Cross-Cutting Themes
- Transparency as a Core Requirement:
Across jurisdictions, transparency is increasingly embedded within regulatory expectations. Whether through administrative law principles, medical device requirements, or AI governance policies, developers are expected to provide documentation, justification, and traceability supporting AI-driven outputs. - Lifecycle Responsibility:
Regulation of healthcare technologies increasingly extends beyond initial approval or deployment. Post-market vigilance, performance monitoring, and change management processes are central to maintaining compliance over time—particularly for software and AI-enabled tools. - Interoperability as Infrastructure:
Digital health modernization efforts emphasize standardized data exchange and secure integration. AI tools that cannot integrate with established interoperability standards risk operational limitations in increasingly connected healthcare environments.
Key Considerations for Regulatory Alignment
For Founders & Business Owners
- [ ] Aligment Analysis: Evaluation of the product roadmap against Pan-Canadian Interoperability Roadmap standards (e.g., Bill S-5).
- [ ] Resource Allocation Benchmarking: Analysis of R&D cycles, noting that “High-Impact” systems often require 15–20% of resources for documentation, risk mitigation, and mandatory reporting.
- [ ] Liability & Risk Scoping: Assessment of professional liability insurance coverage regarding “Algorithmic Malpractice” and “Data Bias” within the context of the AIDA framework.
For Compliance & Regulatory Specialists
- [ ] Submission Infrastructure Readiness: Verification of organizational access to the Common Electronic Submission Gateway (CESG) and alignment of Class II, III, and IV device applications with Mandatory Electronic Regulatory Enrolment (REP) formatting.
- [ ] Algorithmic Impact Benchmarking: Application of the TBS Algorithmic Impact Assessment (AIA) tool to existing products to identify transparency gaps or potential biases.
- [ ] Performance Monitoring Frameworks: Establishment of “Model Drift” logging processes to support “significant change” reporting requirements under current REP rules.
- [ ] Consent Architecture Review: Examination of AI scribe consent protocols, specifically regarding the disclosure of AI processing, data retention, and “Human-in-the-loop” verification steps.
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 - 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
