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Weekly News and Updates (Sept 26 – Oct 3, 2025)

by | Oct 4, 2025 | Uncategorized | 0 comments

Regulatory activity across Canada, the U.S., the EU, and global organizations like WHO and OECD is converging toward a shared vision of safe, transparent, and equitable AI in healthcare. 2025 marks a turning point — from broad policy discussions to operational enforcement and continuous oversight. Health organizations and vendors should now focus on practical readiness: lifecycle monitoring, bias mitigation, explainability, and evidence of compliance across jurisdictions.

Canada

1) Government of Canada: Launch of the AI Strategy Task Force and 30-day national sprint (announced Oct 1, 2025)

The Government of Canada formally announced the creation of an AI Strategy Task Force and a 30-day national sprint (Oct 1–31, 2025) to gather input that will shape Canada’s renewed AI strategy. The Task Force membership and scope (research, talent, commercialization, safe systems, public trust, infrastructure, and security) were published on the official Government of Canada site. The announcement notes the Task Force will consult broadly and share recommendations in November 2025.
Government of Canada — AI Strategy Task Force & national sprint (Oct 1, 2025)

How this relates to AI in healthcare:

  • Funding & priorities may shift quickly: the Task Force’s remit explicitly includes adoption across industry and building safe AI systems—expect federal funding calls or pilot programs that prioritize healthcare use-cases (diagnostics, triage, public health analytics) to appear in the weeks/months after the sprint. Health system partners should monitor ISED/CIHR/PHAC funding portals and prepare concise briefs about priority problems that AI can address.
  • Standards & procurement influence: the federal strategy will likely emphasise interoperability, transparency, and safety. Provincial health purchasers and hospitals should pre-emptively update procurement templates to require vendor attestations on transparency, data governance, and performance monitoring aligned with federal principles.
  • Cross-border alignment: the Task Force’s international cooperation focus increases the likelihood that Canada will pursue harmonization with EU/FDA/UK approaches — helpful for vendors targeting multi-jurisdictional roll-outs but requires readiness to meet diverse documentation requirements (model cards, PCCPs, data provenance).
  • Actionable next steps for health organisations: prepare a 1–2 page “asks” packet for Task Force consultations (if your organization plans to respond to the public sprint), describing real-world data access challenges, evidence gaps, and regulatory friction points for safe health AI deployment.

2) Innovation, Science and Economic Development (ISED) — public consultation page / “Help define the next chapter” (opened Oct 1, 2025)

ISED (the department administering the national sprint) launched a public consultation portal to collect feedback from Canadians between Oct 1–31, 2025. The portal invites input on research & talent, commercialization, scaling, safety/trust, and infrastructure.
Consultation page: ISED — Help define the next chapter of Canada’s AI leadership (open Oct 1–31, 2025)

How this relates to AI in healthcare:

  • Opportunity to influence policy: healthcare providers, clinical researchers, and patient groups should consider responding. Well-documented submissions that include concrete case studies (data-sharing barriers, procurement friction, evidence needs) tend to be prioritized.
  • Suggested submission content: (a) examples of high-impact health AI use-cases; (b) specific regulatory/operational barriers (e.g., data access, privacy interpretation across provinces); (c) requests for federal support (funding for validation cohorts, national data standards, trusted research environments).
  • Immediate action: draft and submit a short position paper (1–3 pages) before Oct 31, emphasising evidence generation and ethical deployment in health settings. This is a low-effort, high-leverage route to shape funding and standards priorities.

3) Office of the Privacy Commissioner of Canada (OPC) — Annual reports & rolling publications (late Sep / early Oct 2025)

The OPC continues to update and publish major documents and rolling pages in late Sep / early Oct (including the OPC site’s “what’s new” and annual reporting material). Around this date the OPC published items highlighting enforcement activities and reporting timelines (the OPC site shows recent activity and the PIPEDA investigations index is actively updated). Example pages: OPC investigations index and annual reporting pages.

OPC — Investigations into businesses

How this relates to AI in healthcare:

  • Enforcement remains active & expanding scrutiny: the OPC’s continued publication activity in late September/early October shows regulators are consolidating enforcement learnings (following high-profile cases such as TikTok). Health AI projects — particularly those using real-world PHI or involving children’s data — should expect focused scrutiny on transparency, de-identification, and lawful bases for processing.
  • Operational guidance implied: the OPC’s enforcement tone implies that privacy program documentation, demonstrable de-identification tests, and transparent patient notices lower regulatory risk. For health AI vendors and hospitals: maintain a mapped, auditable PIA/DPIA for each AI product or pipeline.
  • Immediate action: schedule an internal OPC-readiness review: validate PIAs, confirm cross-border transfer contracts, and prepare a public-facing AI disclosure template for patient portals and clinician-facing UI elements.

 

 

 

 

 

 

 

 

United States

1) FDA – Real-world performance and AI/ML devices (Request for Public Comment, Aug 2025 / Sept-Oct 2025)


In late August 2025, the FDA’s Digital Health Center of Excellence issued a Request for Public Comment: Measuring and Evaluating Artificial Intelligence-Enabled Medical Device Performance in the Real World (Docket No. FDA-2025-N-4203) that remains active into Sept–Oct.

The request seeks feedback on drift detection, performance metrics, post-market monitoring, data sources, triggers, and human oversight.

U.S. Food and Drug Administration

How this relates to AI in healthcare:

  • Developers must plan for continuous performance evaluation — not just static validation — and define drift thresholds and remediation workflows.

  • Hospitals and health systems deploying AI devices should ensure vendor contracts allow access to real-world performance logs, error rates, and model update summaries.

  • This also sets expectations for transparency: vendors may be required to publish performance metrics over time, signal regressions, and provide justification for model updates.

 

 

 

 

2) FDA – Draft Guidance on AI/ML Device Lifecycle & Marketing (2025 draft) continued emphasis


The FDA’s draft guidance “Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations” published Jan 7, 2025, continues to be referenced and updated in industry workflows. It emphasizes including in marketing submissions documentation of risk, validation, transparency, bias, and lifecycle controls.

Draft Guidance – Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations

How this relates to AI in healthcare:

  • For any AI/ML medical device, vendors must build submission packages with comprehensive documentation of development, validation, performance, safety, bias mitigations, retraining plans, and cybersecurity.

  • Early engagement with the FDA (Q-sub meetings) becomes critical to align expectations, especially for novel or adaptive AI systems.

 

 

 

 

 

 

 

 

European Union & United Kingdom

1) EU AI Act & Medical AI (ongoing implementation, clarifications, sector alignment)


The EU AI Act (Regulation (EU) 2024/1689) entered into force August 1, 2024. High-risk AI systems — including medical AI — must comply with strict obligations on data quality, transparency, human oversight, post-market monitoring, and conformity assessment paths.


MedTech Europe has publicly called for clarity, especially on aligning AI Act with existing Medical Device Regulation (MDR/IVDR) frameworks to avoid regulatory overlap and to preserve innovation timelines.

MedTech Europe


Additionally, the European Health Data Space (EHDS) is emerging as a key enabler of safe secondary use of health data, facilitating cross-border data flows under updated governance rules.

Artificial Intelligence in healthcare

How this relates to AI in healthcare:

  • Vendors exporting to EU must prepare to meet dual obligations: MDR/IVDR and AI Act, with harmonized clinical evidence, transparency, risk assessment, and post-market obligations.

  • Health systems within the EU will depend on EHDS infrastructure for data access (training, validation) and must ensure AI systems comply with data governance, privacy, and provenance rules.

  • Canadian vendors aiming for EU market should adopt early compliance practices (model cards, drift monitoring, documented fairness, and submission-ready technical files) ahead of full enforcement.

     

     

     

     

     

     

     

     

    Rest of the World – Key Official Items

     

     

    1) WHO – Strengthening Global Infrastructure for AI in Health Governance (March – Ongoing 2025)

    The World Health Organization (WHO) continues advancing its AI governance agenda, notably through the establishment of a WHO Collaborating Centre on AI for Health Governance at the Delft Digital Ethics Centre. This Centre supports WHO’s efforts to build regulatory and ethical capacity in AI health applications across countries and to develop AI assessment methodologies for clinical and public-health tools.

    WHO Collaborating Centre on AI for Health Governance


    Additionally, WHO’s ongoing Harnessing Artificial Intelligence for Health program and Global Initiative on AI for Health (GI-AI4H) are expanding community-based collaboration between researchers, industry, and regulators.

    WHO Harnessing Artificial Intelligence for Health Program

    How this relates to AI in healthcare:

    WHO’s initiatives aim to harmonize ethical oversight and validation of AI systems in clinical environments. The Collaborating Centre represents a step toward global alignment in AI safety evaluation, complementing regulatory moves in the EU (AI Act) and Canada (AIDA).


    By promoting transparency, algorithmic auditability, and equitable access, WHO’s work directly contributes to reducing global disparities in AI-driven health innovation — particularly in low- and middle-income countries (LMICs).
    The upcoming WHO technical briefs previewed at the AI for Good Summit 2025 are expected to address AI in diagnostics, digital therapeutics, and even traditional medicine, signaling a broadened governance scope.

    AI for Good Summit 2025 – WHO Session: Enabling AI for Health Innovation and Access

     

     

     

     

     

     

     

    Cross-Cutting Themes Across Jurisdictions

    Across Canada, the United States, the European Union, and global governance bodies such as WHO and OECD, several shared patterns are emerging in AI oversight. While each region has unique priorities, all are moving toward a common model emphasizing trustworthy, transparent, and continuously monitored AI systems in healthcare.

    1. Accountability and Lifecycle Oversight
    Regulators increasingly expect developers and healthcare providers to maintain full accountability throughout the AI system’s lifecycle — from development and validation to post-deployment monitoring and retirement. Canada’s forthcoming AI Strategy Task Force and the U.S. FDA’s lifecycle management guidance both emphasize continuous evaluation and drift detection. In the EU, the AI Act introduces ongoing conformity assessments for high-risk medical AI systems, reinforcing the shift from static approval to dynamic oversight.

    2. Transparency, Explainability, and Auditability
    Transparency requirements are becoming universal. Canada’s Office of the Privacy Commissioner (OPC) emphasizes clear documentation of lawful data use, de-identification, and patient communication. The FDA now expects real-world performance reporting for AI-enabled devices, while the EU AI Act mandates detailed documentation on data provenance, intended use, and explainability. WHO and OECD guidance further promotes algorithmic transparency and public accountability, setting expectations for open communication of AI model risks and limitations.

    3. Fairness, Ethics, and Non-Discrimination
    Equity and fairness are no longer aspirational principles — they are enforceable expectations. The U.S. Department of Health and Human Services (HHS) clarified that biased AI in healthcare can violate federal civil rights law. Similarly, OECD AI Principles and WHO’s global recommendations highlight fairness across demographics and call for explicit bias monitoring in health datasets. In Canada and Europe, upcoming AI strategies are expected to include mandatory fairness and equity assessments within procurement and funding frameworks.

    4. Data Governance, Security, and Interoperability
    Robust data management is at the heart of all current AI regulatory efforts. Canada’s AI Task Force, the EU’s European Health Data Space, and WHO’s global initiatives all underscore the importance of secure, standardized, and privacy-preserving data infrastructure. Expect increased scrutiny of data lineage, lawful basis for processing, and international transfer controls. For healthcare systems, aligning with interoperability standards and adopting trusted research environments will be crucial to enable compliant AI development and deployment.

    5. International Alignment and Mutual Recognition
    Major jurisdictions are explicitly pursuing regulatory convergence. Canada’s AI Strategy Task Force references harmonization with the EU, FDA, and UK frameworks. OECD continues to promote common principles of safety, accountability, and human oversight through its AI Policy Observatory, while WHO’s Collaborating Centres on AI for Health Governance help unify technical benchmarks globally. This trend will simplify cross-border market access over time — but it also raises the bar for documentation and transparency.

    6. From Policy to Practice
    2025 marks a transition from principle-based discussion to tangible implementation. Regulators are operationalizing AI oversight through sandboxes (UK MHRA AI Airlock), consultation sprints (Canada’s ISED portal), and collaborative technical hubs (WHO Delft Centre). These developments signal that compliance is no longer theoretical — organizations must demonstrate real-world safety, validation, and accountability practices.

    Immediate, Concrete Checklist for Health Organizations & Vendors

     

    Regulatory convergence means the compliance window is shrinking. Healthcare institutions, developers, and vendors should use the next few months to move from high-level awareness to operational readiness. The following checklist consolidates the actionable steps drawn from current developments in Canada, the U.S., EU, and global health governance bodies.

    1. Governance, Documentation & Oversight

    • Establish an AI Governance Committee that includes compliance, clinical, IT, and legal representatives.

    • Maintain an AI System Register listing all AI tools used in the organization, with version history, risk classification, and validation status.

    • Conduct or update DPIAs/PIAs for each AI system, documenting lawful data use, privacy measures, and cross-border transfer mechanisms.

    • Implement continuous lifecycle monitoring — define model drift thresholds, audit frequency, and escalation triggers for performance anomalies.

    • Assign a responsible officer (e.g., Chief Data Officer, Clinical AI Lead) to oversee post-deployment monitoring and vendor reporting compliance.

    2. Data Management, Validation & Security

    • Map all data flows used in AI training, validation, and operation, including third-party data sources.

    • Validate data quality by checking completeness, representativeness, and annotation accuracy; maintain traceability records.

    • Adopt de-identification and data minimization protocols aligned with OPC, GDPR, and WHO recommendations.

    • Use trusted research environments (TREs) or controlled data enclaves for sensitive health data.

    • Document security controls (encryption, access management, audit logging) for inclusion in AI system technical files.

    3. Fairness, Bias & Ethics

    • Perform bias audits on all AI models — including subgroup performance analysis across demographics (sex, age, ethnicity, socioeconomic status).

    • Integrate fairness constraints or rebalancing strategies into model training where disparities are detected.

    • Document ethical justifications for clinical decision-support systems and ensure outputs are reviewable by clinicians.

    • Publish transparency summaries (akin to “model cards”) detailing datasets used, limitations, and potential bias sources.

    • Engage patients and clinicians in testing and feedback loops to identify and mitigate unintended bias or harm.

    4. Transparency, Explainability & Communication

    • Ensure every AI tool has explainable outputs appropriate to its users (clinicians, administrators, or patients).

    • Develop patient-facing AI disclosures explaining how AI contributes to care decisions, consistent with OPC and HHS guidance.

    • Align internal documentation with EU AI Act templates, including intended purpose, technical documentation, and human oversight mechanisms.

    • Implement audit trails that record decision inputs, system states, and user interactions to support traceability and accountability.

    • Prepare external-facing “AI Fact Sheets” for public trust and regulatory transparency.

    5. Procurement, Contracting & Vendor Management

    • Update procurement templates to require AI vendors to attest to compliance with local and international standards (OPC, FDA, EU AI Act, ISO/IEC 42001).

    • Include clauses on continuous reporting of model performance, updates, and bias monitoring.

    • Require access to raw or aggregated performance logs for independent validation.

    • Mandate cybersecurity disclosure (software bill of materials, patch cadence, known vulnerabilities).

    • Ensure liability and indemnification terms cover potential model malfunctions or biased outcomes.

    6. Training, Capability & Awareness

    • Train staff and clinicians on responsible AI use, limitations, and the institution’s AI governance procedures.

    • Develop standard operating procedures (SOPs) for AI adoption, validation, and incident response.

    • Simulate an AI incident response exercise — e.g., handling of false positives in diagnostic AI or erroneous triage recommendations.

    • Establish a feedback channel for end-users to report anomalies or perceived bias in AI behavior.

    • Monitor emerging regulations monthly — assign a compliance lead to track FDA, Health Canada, EU AI Act, and WHO/OECD publications.

    7. Cross-Border Readiness

    • Align documentation across jurisdictions (Canada’s AIDA principles, FDA guidance, EU AI Act conformity files).

    • Adopt international standards (ISO/IEC 42001 for AI management, ISO 62304 for medical software lifecycle, ISO 13485 for quality systems).

    • Use OECD AI Principles as a unifying baseline for internal policies — focusing on safety, fairness, transparency, and human oversight.

    • Map data residency and transfer obligations — confirm contracts and storage practices align with PIPEDA, GDPR, and HIPAA.

    • Develop an “export-ready” compliance package including conformity statements, validation evidence, and model documentation for each jurisdiction.

    8. Immediate Next 30–60 Days Priorities

    • By October 31: Submit input to ISED’s AI consultation portal and prepare “asks” for Canada’s AI Strategy Task Force.

    • By November: Conduct an internal audit using OPC readiness checklists and FDA real-world performance frameworks.

    • By December: Implement model drift monitoring, bias audit documentation, and vendor reporting processes.

    • By Q1 2026: Align technical documentation and internal AI governance policies with EU AI Act and OECD recommendations.

     

     

     

     

     

     

     

     

    Sources

    Written by Grigorii Kochetov

    Cybersecurity Researcher at AI Healthcare Compliance

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