Monthly News and Updates (June 2026)

During June, 2026, AI-in-healthcare governance advanced on multiple fronts across our tracked jurisdictions. Canada named health and life sciences the first priority sector of its new national AI strategy and committed $300 million in combined health-data infrastructure funding. The US FDA codified a new regulatory pathway for AI-based imaging software that allows algorithm updates without new submissions. The UK’s MHRA launched two regulatory sandboxes and published foundational research ahead of its forthcoming AI-in-healthcare rulebook. The WHO issued new guidance on AI’s role in evidence-informed health policy, and the Joint Commission launched the first voluntary, healthcare-specific AI governance certification in the US.

Disclaimer: This article is produced for educational and informational purposes only. It summarises publicly available regulatory updates and reflects our interpretation of emerging developments; it does not constitute legal, regulatory, financial, or professional advice, nor should it be interpreted as a statement of regulatory intent. Readers should consult qualified counsel before taking any action based on the information presented here.

Canada

1) National AI Strategy “AI for All” names health as first priority sector.

The federal government released Canada’s national artificial intelligence strategy, “AI for All,” which organizes federal AI policy around six pillars and identifies five priority sectors for investment. Health and life sciences is named the first of these priority sectors, on the basis of Canada’s universal health system, world-class research institutions, and fast-growing life sciences sector.

Canada’s national Artificial Intelligence strategy: AI for All

The Six Pillars

• Pillar 1 — Protecting Canadians and Safeguarding Democracy: A safety-first approach grounded in law, including modernized privacy legislation, new online safety laws, protection of elections from AI-enabled misinformation, and a $50 million investment to expand the Canadian AI Safety Institute’s capacity to track risks and evaluate AI models. This pillar also introduces a new Canada Trusted AI Certification program to help Canadians identify trustworthy AI products in the marketplace.

• Pillar 2 — Empowering Canadians: A National AI Literacy Initiative offering entry-level AI training, reaching 1 million post-secondary students and training more than 3,000 educators, with all post-secondary students to receive access to trusted AI agents. This pillar aims to create up to 90,000 AI-related job and work-placement opportunities for young Canadians by 2031.

• Pillar 3 — Powering Shared Prosperity: Support for SMEs moving from AI experimentation to integration, including a $500 million LIFT financing initiative through the Business Development Bank of Canada and $500 million to expand the Regional Artificial Intelligence Initiative. This pillar also launches the new AI Missions Program, whose first mission commits $200 million to improving health outcomes for Canadians.

• Pillar 4 — Building the Canadian Sovereign AI Foundation: Investment in sovereign compute, data, and talent, including a world-leading public supercomputer, $100 million to launch a Health Sector Data Space with the Canadian Institute for Health Information (CIHI), and $100 million to expand the VITAL clinical-data platform into five additional provinces.

• Pillar 5 — Scaling Canadian Champions: A $500 million Canadian Tech Growth Fund enabling the federal government to take equity stakes in promising Canadian AI firms, plus $700 million in additional sovereign compute access for SMEs, aimed at keeping Canadian AI companies and their intellectual property anchored in Canada as they scale.

• Pillar 6 — Building Trusted Partnerships and Global Alliances: Expansion of the newly formed Canada-Germany Sovereign Technology Alliance and continued development of the 11 AI-related international partnerships Canada has signed across four continents, spanning AI safety standards, sovereign infrastructure, and industrial deployment.

The Five Priority Sectors

• Health and life sciences — named the first priority sector, with AI expected to expand access to primary care, reduce ER wait times, and lighten administrative burden on physicians.

• Energy and natural resources — using AI to optimize extraction, accelerate the clean energy transition, and secure critical mineral supply chains.

• Transportation — intelligent logistics, autonomous systems, and predictive infrastructure maintenance.

• Agriculture — AI-powered precision farming to increase yields and strengthen food security.

• Manufacturing and robotics — industrial AI and robotics to address labour shortages and reshoring pressures.

Key Actions Most Relevant to Health AI

• Launch a new AI Missions Program to advance targeted, high-impact projects; the first mission commits $200 million to improving health outcomes for Canadians, with more missions to follow in other sectors.

• Invest $100 million to launch the Health Sector Data Space, in partnership with CIHI, to link secure, standardized datasets for clinical trials, health services research, and performance measurement.

• Invest $100 million to expand the VITAL clinical-data platform into five additional provinces, building on its existing network of 160 hospitals across Ontario, Alberta, and Quebec.

• Invest $50 million to expand the Canadian AI Safety Institute’s capacity to evaluate AI models, which is expected to extend to safety evaluation of clinical and health-adjacent AI systems.

• Create a Canada Trusted AI Certification program to help Canadians, including patients and clinicians, identify trustworthy AI products in the marketplace.

• Build a world-leading public supercomputer, giving researchers and SMEs, including health AI developers, access to secure, sovereign, high-performance compute.

How it applies to AI in Healthcare:

The strategy identifies health and life sciences as the first priority sector, signalling that health AI is expected to play a central role in Canada’s AI policy agenda.

2) $100M federal investment to expand the VITAL health data platform.

Announced under the new AI strategy, the government committed a further $100 million to expand VITAL, a pan-Canadian clinical-data network that securely connects near real-time, de-identified hospital data. The first phase of VITAL already connects 160 hospitals across Ontario, Alberta and Quebec, serving over 20 million Canadians; this investment is intended to expand participation into additional provinces and territories, strengthening clinical trials, health services research, and AI-enabled diagnostics such as sepsis detection and heart disease prediction. Each province and territory retains ownership and oversight of its own hospital data, with governance frameworks aligned to federal and provincial health-data protection standards, and Indigenous-led governance provisions for Indigenous data.

Government of Canada invests $100 million in VITAL health data platform

How it applies to AI in Healthcare:

VITAL’s expansion is significant for any organization building or validating clinical AI models on Canadian data, since it is intended to expand research and innovation while keeping governance decentralized at the provincial level. Companies developing diagnostic, triage, or monitoring AI tools should track which provinces join VITAL next, as participation will determine where multi-site validation and deployment partnerships become feasible, and should note the platform’s Indigenous data governance provisions when working with data that touches Indigenous communities.

US

1) FDA classifies new AI imaging device category with predetermined change control plan.

The FDA issued a final order classifying “radiological machine learning-based quantitative imaging software with predetermined change control plan” as a Class II device under new special controls, codified at 21 CFR 892.2055. The device type covers software-only tools that use machine learning on radiological images to produce quantitative outputs such as view selection, segmentation, and landmarking. Critically, the classification formally recognizes predetermined change control plans (PCCPs), allowing manufacturers to make pre-authorized algorithm modifications without submitting a new premarket notification for each update, provided the special controls around design verification, risk management, and labeling are met. The classification stems from a De Novo request originally granted to Caption Health’s automated ejection fraction software, and the device type can now serve as a predicate for future 510(k) submissions.

Medical Devices; Radiology Devices; Classification of the Radiological Machine Learning-Based Quantitative Imaging Software With Predetermined Change Control Plan

How it applies to AI in Healthcare:

This creates a defined regulatory pathway for a specific category of adaptive AI imaging tools, and it gives other manufacturers a predicate device type to reference in 510(k) submissions rather than defaulting to Class III premarket approval. Compliance and regulatory teams working on quantitative imaging software should evaluate whether their device fits this new classification, and should build PCCP documentation, including training data descriptions, performance testing protocols, and version-history labeling, into their design controls from the outset, since PCCP-eligible changes still require the special controls specified in the order to avoid new submissions.

EU & UK

1) MHRA launches AI regulatory sandbox for medicines development.

The Medicines and Healthcare products Regulatory Agency (MHRA) announced a new regulatory sandbox giving companies and researchers a controlled environment to work directly with regulators while testing AI tools that predict how medicines behave in the body, including absorption, metabolism, and potential safety risks. Up to five AI-driven approaches will be tested in the first phase, beginning summer 2026, with the programme also examining how better use of clinical data can improve understanding of medicine effects across underrepresented groups such as children and older people. The initiative is funded through the UK Government’s Regulatory Innovation Office and supports the government’s broader push to reduce reliance on animal testing and to deliver the “AI for Science Mission One” and 10 Year Health Plan goals.

MHRA launches AI sandbox to accelerate medicines development and improve safety

How it applies to AI in Healthcare:

For life sciences and health AI companies developing predictive safety or pharmacokinetic models, this sandbox offers a rare opportunity to build an evidence base directly alongside a regulator, which may help developers generate evidence that could support future regulatory submissions. Compliance teams should monitor how MHRA defines “sufficient evidence” for these tools during the pilot, since the standards developed here are likely to inform broader expectations for AI-supported drug development submissions in the UK.

2) MHRA publishes National Commission research and Call for Evidence findings.

MHRA published two reports commissioned to support the independent National Commission into the Regulation of AI in Healthcare: a Research and Engagement report drawing on input from patients, the public, healthcare professionals, and industry, and the findings of a Call for Evidence that drew responses from 760 people and institutions. Key themes included strong support for ongoing post-deployment monitoring of AI tools’ performance and safety, the need to balance rigorous regulation with the pace required for patients to benefit quickly, and public expectations of transparency, accountability, and human oversight. These findings will directly inform the National Commission’s recommendations, expected later in the summer, which will shape the MHRA’s forthcoming AI-in-healthcare regulatory rulebook.

MHRA landmark report reveals public views on AI in healthcare

How it applies to AI in Healthcare:

These reports are a leading indicator of what the UK’s future AI-in-healthcare regulatory framework will prioritize: continuous post-market monitoring, transparency, and human oversight appear likely to influence future regulatory expectations. Organizations planning to deploy AI in UK healthcare settings should begin building post-deployment monitoring and transparency documentation now, ahead of the National Commission’s formal recommendations later this summer.

3) MHRA and NHS England launch “London Region I” AI medical device sandbox.

MHRA, in partnership with NHS England (London) and the three London Health Innovation Networks, launched a new real-world deployment sandbox called London Region I. Up to 10 AI medical device manufacturers will be selected for the initial phase to deploy their technologies in live clinical settings across London NHS providers under MHRA oversight, generating real-world evidence on safety and effectiveness to support a clearer route to wider adoption. MHRA will invite expressions of interest from both NHS providers and AI medical device manufacturers the following month.

Pioneering AI health innovations regulatory sandbox launched

How it applies to AI in Healthcare:

This gives AI medical device manufacturers a concrete, regulator-supervised route to generate real-world evidence within actual NHS clinical settings, which may help generate real-world evidence relevant to future regulatory and adoption decisions. Companies with AI-enabled devices at a late stage of development should watch for MHRA’s call for expressions of interest and prepare evidence packages demonstrating readiness for controlled, outcomes-based deployment.

Rest of the World

1) WHO discussion paper on AI in evidence-informed health policy.

The World Health Organization published a discussion paper, “Artificial intelligence and evidence-informed policy – emerging challenges and opportunities,” examining how AI is reshaping the way health policy problems are defined, solutions are designed, and impact is evaluated. The paper maps distinct risks at each stage of the policy cycle, including data bias skewing problem definition, over-optimization narrowing solution design, and subtle monitoring biases gradually shifting policy away from original goals, and highlights a cross-cutting concern about AI privileging quantifiable data over lived experience, local expertise, and Indigenous knowledge. It draws on WHO’s AI ethics guidance, the GRADE Evidence-to-Decision framework, FAIR data principles, and the OECD AI Principles to recommend practical governance tools such as algorithmic impact assessments, human-in-the-loop decision gateways, and multidisciplinary oversight panels, and it calls for a common governance framework across WHO Member States.

New WHO discussion paper sets out opportunities and risks of AI in evidence-informed health policy

How it applies to AI in Healthcare:

While framed around policy-making rather than clinical care, the discussion paper suggests that AI governance principles, such as transparency, human oversight, and risk-based review, could extend beyond direct patient care into the health-policy and regulatory-evidence pipeline itself. Organizations engaging with health ministries or regulators on AI-enabled research or evidence generation should be prepared to demonstrate how their tools support, rather than substitute for, human judgment in framing questions and interpreting results.

Frameworks & Certifications

1) Joint Commission launches “Responsible Use of AI in Healthcare” (RUAIH) certification.

The Joint Commission launched RUAIH, a new voluntary certification program recognizing US healthcare organizations that demonstrate governance, safeguards, monitoring processes, and staff education for the responsible use of AI. The program builds on initial AI guidance the Joint Commission released in 2025 after convening more than 20 healthcare and technology coalitions, and it treats responsible AI use as a patient safety, quality, governance, privacy, and trust issue rather than a purely technical one. RUAIH covers areas including governance structures, data management, risk and bias reduction, ongoing monitoring, and staff transparency and training, and it is open to any US healthcare organization regardless of existing Joint Commission accreditation status.

Responsible Use of Artificial Intelligence in Health Care Certification

How it applies to AI in Healthcare:

RUAIH is the first healthcare-specific, voluntary AI governance certification of its kind in the US, and it gives health systems a structured, externally validated way to demonstrate AI governance maturity to patients, payers, and partners, independent of general Joint Commission accreditation. Health AI vendors may wish to anticipate prospective healthcare customers to increasingly ask about RUAIH alignment during procurement, and compliance teams at both vendors and health systems should map their existing AI governance, monitoring, and bias-mitigation practices against RUAIH’s criteria to identify gaps ahead of a formal certification pursuit.

Cross-Cutting Themes

Disclaimer: The cross-cutting themes below are analytical observations based on the sources cited in this article and reflect our interpretation of emerging regulatory and policy developments. They do not constitute legal, regulatory, or professional advice, nor should they be interpreted as statements of regulatory intent. Readers may wish to consult qualified advisors regarding the application of any regulatory requirements to their specific circumstances.

• Regulatory sandboxes are becoming an increasingly common tool for AI health governance. The UK deployed two sandboxes in a single week (medicines development and medical devices), following a growing international pattern of controlled, real-world testing environments as a bridge between innovation and formal approval.

• Governments are funding health data infrastructure as AI infrastructure. Canada’s combined $200 million in health-data platform investment (VITAL and the Health Sector Data Space) reflects a broader recognition that AI progress in healthcare is gated by access to standardized, governed clinical data, not just algorithms.

• Adaptive AI is beginning to receive more tailored regulatory pathways. The FDA’s predetermined change control plan classification for imaging software formalizes a lighter-touch route for continuously updating AI models, a structural shift away from treating every algorithm update as a new device.

• Voluntary certification is emerging as a governance layer between hard law and internal policy. The Joint Commission’s RUAIH certification shows healthcare-specific accreditation bodies stepping in to standardize AI governance expectations ahead of, or alongside, formal regulation.

• Public trust and transparency are consistently named as prerequisites for adoption, not afterthoughts. Both the UK’s National Commission research and the WHO’s policy paper independently converge on human oversight, ongoing monitoring, and transparency as the core conditions for public and institutional trust in health AI.

Key Considerations for Regulatory Alignment

For Founders & Business Owners

• Track new funding and sandbox windows. Canada’s AI Missions Program, VITAL’s provincial expansion, and MHRA’s two new sandboxes all represent near-term windows to access funding, data, or regulator collaboration; early engagement can shape favorable terms before programs formalize.

• Evaluate whether your device fits the new FDA imaging classification. If you build quantitative radiological imaging software with plans for iterative model updates, assess whether structuring your submission around a predetermined change control plan under 21 CFR 892.2055 could reduce your long-term regulatory burden.

• Consider preparing for voluntary certification expectations. RUAIH is voluntary today, but as with prior Joint Commission certifications, it may become a de facto procurement requirement; building toward its criteria now can be a competitive differentiator in US health system sales.

For Compliance & Regulatory Specialists

• Prepare PCCP documentation as a standard design-control artifact. For any AI imaging tool with a US regulatory path, build training data descriptions, performance testing protocols, and change-management documentation into your quality system now, rather than retrofitting it for a future submission.

• Monitor the UK National Commission’s forthcoming recommendations. The Research and Engagement findings published this month strongly foreshadow post-market monitoring and transparency requirements; organizations operating or planning to operate in the UK should begin building these capabilities ahead of formal publication later this summer.

• Map data governance practices against VITAL’s model. VITAL’s approach, provincial ownership of data paired with standardized de-identification and Indigenous-led governance provisions, is a useful reference point for any organization designing multi-jurisdictional health data-sharing agreements in Canada.

Sources

• Government of Canada – Canada’s National Artificial Intelligence strategy: AI for All (Modified: June 8, 2026)
  https://ised-isde.canada.ca/site/ised/en/canadas-national-artificial-intelligence-strategy-ai-all

• Government of Canada – Government of Canada invests $100 million in VITAL health data platform (Published: June 23, 2026)
  https://www.canada.ca/en/innovation-science-economic-development/news/2026/06/government-of-canada-invests-100-million-in-vital-health-data-platform.html

• Federal Register – Medical Devices; Radiology Devices; Classification of the Radiological Machine Learning-Based Quantitative Imaging Software With Predetermined Change Control Plan (Effective: June 17, 2026)
  https://www.federalregister.gov/documents/2026/06/17/2026-12166/medical-devices-radiology-devices-classification-of-the-radiological-machine-learning-based

• Gov UK – MHRA launches AI sandbox to accelerate medicines development and improve safety (Published: June 9, 2026)
  https://www.gov.uk/government/news/mhra-launches-ai-sandbox-to-accelerate-medicines-development-and-improve-safety

• Gov UK – MHRA landmark report reveals public views on AI in healthcare (Published: June 11, 2026)
  https://www.gov.uk/government/news/mhra-landmark-report-reveals-public-views-on-ai-in-healthcare

• Gov UK – Pioneering AI health innovations regulatory sandbox launched (Published: June 10, 2026)
  https://www.gov.uk/government/news/pioneering-ai-health-innovations-regulatory-sandbox-launched

• WHO – New WHO discussion paper sets out opportunities and risks of AI in evidence-informed health policy (Published: June 2, 2026)
  https://www.who.int/news/item/02-06-2026-new-who-discussion-paper-sets-out-opportunities-and-risks-of-ai-in-evidence-informed-health-policy

• Join Comission – Joint Commission Releases First of Its Kind Exclusively Designed for Healthcare Organizations, Voluntary Responsible Use of AI in Healthcare Certification (Published: June 1, 2026)
  https://www.jointcommission.org/en-us/knowledge-library/news/2026-05-responsible-use-of-ai-in-healthcare-certification