Canada
1) OPC News Release – October 10, 2025
The Office of the Privacy Commissioner (OPC) published a news release on October 10, 2025, spotlighting its ongoing regulatory priorities, including increased scrutiny of AI and digital data uses.
Office of the Privacy Commissioner of Canada – News release (Oct 10, 2025)
How this relates to AI in healthcare:
- This announcement signals that AI systems processing personal health information (PHI) are under heightened OPC attention. Health organizations deploying AI should ensure rigorous transparency, justification of inferences, and adherence to privacy principles (e.g. data minimization, purpose limitation).
- AI projects that include biometric, diagnostic, or predictive algorithms must revisit their privacy impact assessments (PIAs / DPIAs), ensuring that the AI-specific risk layers (e.g. model explainability, algorithmic bias, data linkage) are addressed.
- The OPC’s focus may translate to future investigations, audits or guidance on AI in healthcare; proactive alignment is preferable to reactive compliance pressure.
2) FPT (Federal / Provincial / Territorial) Annual Meeting – October 2025
The 2025 annual meeting of federal, provincial, and territorial commissioners and ombuds (FPT) was convened, including topics such as artificial intelligence and cybersecurity risks among its agenda items.
Office of the Privacy Commissioner of Canada – FPT events / provincial-territorial collaboration (2025)
How this relates to AI in healthcare:
- Provincial health information acts (e.g. Ontario’s PHIPA, Quebec’s Act respecting health services and social services, etc.) are under the purview of provincial privacy authorities; coordination in FPT means harmonized expectations for AI in health across provinces is more likely.
- A provincial health data custodian or hospital using AI should expect that privacy regulators in all relevant provinces may jointly align policy expectations for algorithmic audit, vendor contracts, and data sharing safeguards.
- Because the FPT agenda included AI, future joint guidance or harmonized standards (e.g. a pan-Canadian AI in health privacy benchmark) could emerge – organizations deploying AI should monitor forthcoming joint outputs from these bodies.
3) OPC Operational Priorities (2025-26) — June 2025 baseline
While not newly published in October, the OPC’s 2025-26 planning documents (released June 2025) remain highly relevant to interpreting recent signals and priorities. The document outlines OPC commitment to monitoring emerging technologies, including AI, and raising public awareness and regulatory readiness.
Office of the Privacy Commissioner of Canada’s 2025-26 Departmental Plan
How this relates to AI in healthcare:
- These baseline plans show that AI is a standing OPC priority, not a transient focus — healthcare AI deployments should be designed with long-term compliance and monitoring in mind.
- Funding and research programs supported by OPC (e.g. research contributions) may generate empirical studies or guidance on AI, which healthcare organizations may use for benchmarking or audit.
Office of the Privacy Commissioner of Canada – Contributions Program (research funding) - Healthcare AI actors should consider applying to such research funding or at least monitoring outputs from OPC-supported research, especially in privacy, algorithmic fairness, and governance of AI in health contexts.
United States
The U.S. FDA continues to maintain active engagement on AI / ML software in medical devices (SaMD) through its digital health programs, guidance work, and advisory committee reviews. Though no new landmark regulation appeared in early October 2025 specifically for AI in healthcare, the ongoing expectations around lifecycle change management, post-market monitoring, and transparency remain central to U.S. regulatory posture.
U.S. Food & Drug Administration – Artificial Intelligence / ML Software as a Medical Device (SaMD)
How this relates to AI in healthcare:
- Vendors targeting the U.S. and Canadian markets should ensure their AI systems adhere to FDA’s expectations for predetermined change control plans (i.e. how model updates are handled), continuous performance monitoring, and transparency to users and regulators.
- Given the FDA’s precedent-setting role, Canadian regulators or purchasers may look to FDA standards as a benchmark; mapping to FDA guidance helps with cross-jurisdictional acceptance and credibility.
- Healthcare organizations deploying AI systems in Canada that also aim for U.S. regulatory compliance should integrate U.S. regulatory compliance checks into their validation, monitoring, and audit frameworks to reduce duplication or regulatory surprises.
European Union & United Kingdom
1) UK – MHRA / Digital Reforms & AI in Trial Approvals (Oct 7, 2025)
The UK’s Medicines and Healthcare products Regulatory Agency (MHRA) published a press release on October 7, 2025, highlighting that clinical trial approval times in the UK have been halved (from ~91 days down to ~41 days) following reforms that integrate AI and digital platforms into regulatory review.
MHRA – UK clinical trial approval times twice as fast with AI and reforms (Oct 7, 2025)
How this relates to AI in healthcare:
- This is an example of regulators themselves using AI to support regulatory review – it demonstrates that AI is not merely a target of regulation but also a tool for regulatory efficiency.
- Health research organizations and AI vendors in Canada can cite this precedent when proposing AI-assisted review workflows for regulatory or ethics review boards (e.g. clinical trial design, document review automation).
- The MHRA describes two bespoke AI tools: the “Knowledge Hub” (spotting common issues in prior applications) and “GMP Compliance Checker” (automating manufacturing documentation checks). Those illustrate feasible architectures for AI in regulatory review that other jurisdictions may emulate.
2) US-UK Regulatory Collaboration (circa Oct 8-9, 2025)
In statements around October 8–9, 2025, MHRA leadership announced efforts to deepen collaboration with the U.S. FDA regarding medical technologies and AI. This includes exploring reliance frameworks and reciprocal recognition for approvals.
GOV.UK – Patients to benefit as UK and US regulators forge new collaboration on medical technologies and AI (Oct 2025)
How this relates to AI in healthcare:
- Cross-recognition or reliance of FDA and MHRA decisions could reduce duplication of regulatory burden for AI medical device vendors; Canadian vendors may see indirect pressure to have FDA/MHRA clearance as leverage in Health Canada engagements.
- The stated alignment of policy development suggests that future AI/medtech regulation may converge across US, UK, and (by extension) Canada – this increases the value of consistency in validation, safety, and transparency strategies.
- Health organizations partnering with international trials or vendors should monitor how reliance or mutual recognition evolves, as it will affect which regulatory reviews are required and how to plan timelines and compliance documents.
3) European Medicines Agency (EMA) – Ongoing AI / Data Strategy Work (October 2025)
The EMA continues to maintain active AI / data strategy pages and public consultations, emphasizing data quality, structured data use, model validation, and post-market surveillance across the medicines lifecycle.
EMA – Artificial intelligence (AI) / data strategy (October 2025)
How this relates to AI in healthcare:
- For AI systems used in drug discovery, personalized medicine, pharmacovigilance, or diagnostics, EMA’s expectations around transparency, data lineage, risk classification and real-world performance monitoring are relevant benchmarks.
- Canada-based vendors or health organizations with cross-EU ambitions should map their AI systems to both EMA’s emerging guidance and Canadian regulatory paths to ensure seamless integration and cross-acceptance.
- Health organizations using AI for pharmacovigilance, adverse event prediction, or clinical decision support should monitor EMA consultation outputs and integrate plan adjustments accordingly.
Rest of the world
In the October 4–13, 2025 window, there were no major new statute-level AI in healthcare laws widely publicized outside of the U.S., UK, EMA, and Canada. Many countries remain in consultation, pilot, or strategy phases. Nevertheless, global regulator momentum continues – for example the EMA network published a data strategy for health data and medicines’ lifecycle, reinforcing global expectations of robust data practices and cross-border data governance.
How this relates to AI in healthcare:
- Even absent new statutes, health AI vendors and organizations should treat global best practices (transparency, lifecycle validation, bias testing) as de facto floor standards.
- Emerging reliance frameworks and regulatory cooperation abroad increase pressure on jurisdictions like Canada to adopt aligned, interoperable regulatory expectations.
Cross-Cutting Themes Across Jurisdictions
- Regulators using AI in their own processes: The MHRA example shows that regulators are embedding AI in review workflows (document scanning, consistency checks) to accelerate approval. This blurs the line between regulated and regulator tool use. Healthcare AI developers should anticipate scrutiny of not only external outputs but also internal model logic transparency.
- Transparency & public summaries: Regulators increasingly expect plain language summaries, public disclosures of model use, and documentation of limitations (especially in health or clinical use cases).
- Lifecycle governance & change control: Across FDA, EMA, and UK signals, prespecification of how AI models may evolve (updates), retraining plans, rollback plans, and monitoring are core expectations.
- Reliance & mutual recognition: The trend toward reliance frameworks (FDA/MHRA, etc.) means that obtaining clearance in one jurisdiction can bolster regulatory arguments in others – but only if traceability, transparency, and validation are robust.
- Privacy, data governance, and algorithmic fairness: Privacy regulators (e.g. OPC in Canada) are more explicitly flagging AI as a risk vector in data processing. Health AI systems must incorporate bias testing, de-identification, differential privacy, and accountability mechanisms.
- Operational acceleration expectations: Regulators are demonstrating that, when AI and digital systems are used thoughtfully, regulatory processes can run faster without compromising safety – increasing expectations that AI in healthcare is not just safe but also efficient.
Immediate, Concrete Checklist for Health Organizations & Vendors
- Revisit and update your DPIA / PIA / Privacy Impact Assessment for AI systems, especially those handling PHI. Explicitly document AI-specific risks (bias, explainability, drift, third-party model dependencies).
- Define change control / model update policies – ensure your AI system has predefined processes for versioning, rollbacks, validation, user impact assessment, and documentation of changes.
- Integrate performance monitoring & bias checks – establish metrics stratified by demographic groups, monitor drift over time, and log all predictions for auditability.
- Include regulatory alignment mapping – map your AI system’s design, validation, and reporting to FDA, EMA, MHRA, and Canadian privacy / health authority expectations; include crosswalks and justification.
- Contract and procurement safeguards – require audit rights, transparency obligations, incident reporting, algorithm recourse, data lineage and algorithmic explainability clauses in vendor contracts.
- Prepare public transparency materials – plain language model description, disclosed limitations, intended use, performance metrics, and user guidance should be prepared ahead of regulatory expectation.
- Engage with regulators early – for clinical trial or AI device projects, propose use of digital/AI tools for review and submit pilot proposals; cite MHRA precedent to reduce approval timelines.
- Track FPT and OPC outputs in Canada – since the FPT agenda includes AI, monitor forthcoming joint guidance, harmonization efforts, or pan-Canadian AI policy publications.
- Participate in research / standards efforts – align with OPC-funded research programs, national AI in health consortia, standards bodies (ISO, IEEE), or data governance consortia to stay ahead of evolving norms.
Sources
- Office of the Privacy Commissioner of Canada – News release (Oct 10, 2025)
https://www.priv.gc.ca/en/opc-news/news-and-announcements/2025/nr-c_251010/ - Office of the Privacy Commissioner of Canada – FPT events / provincial-territorial collaboration (2025)
https://www.priv.gc.ca/en/about-the-opc/what-we-do/provincial-and-territorial-collaboration/fpt-events/fpt-2025/ - Office of the Privacy Commissioner of Canada – 2025-26 Departmental Plan
https://www.priv.gc.ca/en/about-the-opc/opc-operational-reports/planned-opc-spending/dp-index/2025-2026/dp-2025-26/ - Office of the Privacy Commissioner of Canada – Contributions Program (research funding)
https://www.priv.gc.ca/en/opc-actions-and-decisions/research/funding-for-privacy-research-and-knowledge-translation/cp_bg/ - U.S. Food & Drug Administration – Artificial Intelligence / ML Software as a Medical Device (SaMD)
https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device - MHRA / GOV.UK – UK clinical trial approval times twice as fast with AI and reforms (Oct 7, 2025)
https://www.gov.uk/government/news/uk-clinical-trial-approval-times-twice-as-fast-with-ai-and-reforms - GOV.UK – Patients to benefit as UK and US regulators forge new collaboration on medical technologies and AI (Oct 2025)
https://www.gov.uk/government/news/patients-to-benefit-as-uk-and-us-regulators-forge-new-collaboration-on-medical-technologies-and-ai - EMA – Artificial intelligence / data strategy (October 2025)
https://www.ema.europa.eu/en/about-us/how-we-work/data-regulation-big-data-other-sources/artificial-intelligence
